Cities all throughout the world are experiencing fast urbanisation and population growth. Global urban growth patterns indicate that more than half of the world's population already lives in cities, and that number is expected to increase significantly over the next few decades. In today's cities, there is increasing pressure on housing, infrastructure, energy resources, transportation systems, and environmental sustainability (Braun and Clarke 2006).

The traditional approaches to urban planning and management are increasingly unable to address these complex problems. In recent decades, the concept of the "smart city" has emerged as a novel approach to improve urban sustainability, efficiency, and quality of life. Smart cities integrate digital technology, data analytics, and intelligent infrastructure into urban processes to enhance public services, optimise resource management, and fortify governance (Lund et al. 2021).

Cities have shown that they gather and examine vast amounts of data produced by urban activities because to technologies like artificial intelligence, big data analytics, cloud computing, the Internet of Things (IoT), and geographic information systems (GIS). With the use of these technologies, governments can better address social, economic, and environmental issues and keep an eye on urban systems in real time (Khan et al. 2017).

Although interest in creating smart cities is growing, most of the research that is now available focuses on freshly constructed smart cities rather than modifying pre-existing urban landscapes. However, the great majority of people on the earth live in cities with sophisticated infrastructure, complex social structures, and historical urban fabrics. Consequently, transforming existing cities into smart cities is one of the main issues facing contemporary urban development (Braun and Clarke 2006).

Through an integrated framework comprising three interrelated dimensions—digital information infrastructure, environmental urban systems, and social interaction and governance—this study seeks to investigate the requirements and procedures of converting current cities into smart cities.

The idea of "smart cities" has been explored in existing literature in the fields of sustainability, information technology, and urban planning. The influence of digital infrastructure and telecommunications networks on urban growth was highlighted in early research. Subsequent studies extended the idea to encompass citizen involvement, environmental sustainability, and government (Miloud et al. 2024).

Smart cities are urban areas that use digital technologies and intelligent systems to improve infrastructure performance, optimise resource management, and enhance service delivery. Information and communication technology enable cities to monitor and manage energy systems, transportation networks, waste management, and environmental quality (Kong and Chen 2025), (Hall 1999) & (Raheem et al. 2026).

Recent research indicates that artificial intelligence plays a significant role in the development of smart cities. Thanks to AI technologies, this study analyses urban data to provide anticipated insights. Applications including intelligent traffic management, energy optimisation, predictive infrastructure maintenance, and environmental monitoring are supported by these technologies (Shen and Yang 2026), (Zhang and Su 2026), (SIPAHI and SAAYI 2024) & (Qian et al. 2026).

However, concerns regarding potential risks associated with smart city technologies have also been voiced by Lartey & Law (2025). These concerns include the digital divide, data security, privacy, and technical inequality. As a result, many academics emphasise how important it is to find a balance between technical innovation, ethical governance, and social inclusion.

Despite the abundance of literature on smart cities, there is still a significant study gap regarding the transformation of existing cities into smart cities. Most metropolitan regions need to change their existing infrastructure, governance structures, and spatial configurations to accommodate digital technology and intelligent urban management.

The framework is created by conceptually integrating current smart city models, and it is then improved by comparing and contrasting a few chosen case studies.

The qualitative research methodology used in this study is founded on conceptual analysis and a survey of the literature. There are three primary phases to the approach.

The first step was a thorough analysis of scholarly works on smart cities, digital urban transformation, artificial intelligence, and sustainable urban development. The primary theoretical underpinnings and conceptual frameworks utilised in smart city research were identified with the aid of this evaluation.

The study also looks at the key components required to transform existing cities into smart cities. These components were discovered by contrasting previous research with global smart city initiatives.

Third, a thorough conceptual framework is released to explain how existing cities could transition to smart city models. The idea emphasises the interplay between digital infrastructure, environmental sustainability systems, and social involvement.

By using this method, the study is able to provide a comprehensive theoretical model that can support the development of further empirical research and policy.

The data gathered for this study is analysed using a qualitative theme analysis method. After selecting important themes and sub-themes pertaining to the transition of smart cities, the analysis is divided into major indicators, such as infrastructure, data systems, artificial intelligence, and governance.

This study uses a variety of data collection techniques, such as conceptual integration, case study analysis, and literature evaluation. Table 1 provides an overview of the data sources, analytical goals, and results related to each approach in order to improve clarity and openness.

This study adopts a comparative case study approach following established methodological frameworks (Yigitcanlar 2023).

The creation of the suggested framework for smart city transformation is supported by the methodical synthesis of theoretical and empirical insights made possible by the combination of different approaches.

Infrastructure, data, artificial intelligence, and governance are the four interrelated layers that make up the suggested Smart City Transformation Framework. To improve analytical clarity and practical usefulness, each layer is further broken down into distinct sub-components (Khan et al. 2024) & (Machado et al. 2023).

Urban sensing technologies, energy infrastructure, and smart mobility systems are all part of the infrastructure layer. Urban data platforms, cloud-based systems, and digital twin technologies that facilitate real-time data integration and analysis make up the data layer (Rahbarianyazd 2024) & (Yigitcanlar 2023), (Xu et al. 2025) & (Angelidou et al. 2019).

Intelligent urban management is made possible by the artificial intelligence layer, which includes decision-support systems, machine learning algorithms, and predictive analytics. The governance layer consists of venues for citizen participation, smart governance processes, and techniques for integrating policies (Angelidou et al. 2019), & (Kitchin et al. 2019).

By combining institutional and technological aspects into a cohesive framework created especially for modernising cities, this multi-layered structure makes a unique contribution.

Figure 1 represents the framework for smart city components.

The development of smart cities is based on digital information infrastructure. Communication networks, data platforms, cloud computing systems, geographic information systems, and digital governance platforms are all included (Anthopoulos et al. 2022) & (Raheem et al. 2026).

Large amounts of data are produced by sensors, mobile devices, surveillance systems, and digital platforms in modern cities. City officials can keep an eye on traffic patterns, energy usage, environmental conditions, and public service requests in real time by integrating AI and big data analytics (Yang et al. 2024).

Cities may build interconnected systems with data continuously flowing between infrastructure, government agencies, and individuals, thanks to digital infrastructure. Cities are able to enhance decision-making procedures and optimise urban services through this integration (Bibri 2022).

Additionally, digital platforms and broadband connectivity enable residents to engage with public institutions more successfully, access government services online, and take part in urban decision-making processes (Kong and Chen 2025).

One of the most important aspects of developing smart cities is environmental sustainability. Reducing pollution, increasing energy efficiency, and encouraging sustainable resource management are the goals of smart environmental systems.

Cities can monitor trash production, water use, noise levels, and air quality thanks to cutting-edge technologies like IoT sensors, environmental monitoring systems, and AI-based analytics. These technologies offer useful information that can help with policy creation and environmental planning (Khan et al. 2024).

Sustainable urban development also heavily relies on smart energy systems. The analysis highlights more successfully incorporate renewable energy sources and monitor electricity consumption thanks to intelligent energy infrastructures. Energy distribution may be optimised, and overall consumption can be decreased with AI-based energy management systems (Raheem et al. 2026).

Additionally, greenhouse gas emissions and traffic congestion are lessened by smart transportation systems. Urban mobility and environmental sustainability are enhanced by shared mobility services, intelligent traffic management, and intelligent public transit networks (Machado et al. 2023).

Two crucial aspects of the transition of smart cities are social interaction and governance. In addition to relying on technology infrastructure, smart cities should encourage social inclusion, public participation, and open governance (Khan et al. 2017).

Citizens can interact with government agencies, obtain public services, and take part in urban decision-making processes thanks to digital platforms. E-governance systems lower bureaucratic obstacles and increase administrative effectiveness.

In order to make sure that smart city efforts address the needs of locals, citizen participation is crucial. Communities can offer suggestions and comments on urban development initiatives through participatory planning platforms (Miloud et al. 2024).

Furthermore, laws that guarantee cybersecurity, data privacy, and fair access to digital technology are necessary for smart governance. To avoid technological disparity across various social groups, it is imperative to address the digital gap (SIPAHI and SAAYI 2024).

Artificial intelligence is becoming important for managing and optimising urban systems. AI technologies enable cities to handle enormous volumes of data and generate predictive insights that support decision-making.

AI is capable of analysing traffic patterns and optimising signal timing in transportation systems to lessen congestion. Machine learning algorithms can increase power distribution efficiency and forecast electricity demand in energy systems (Li et al. 2023).

By evaluating sensor data from buildings, bridges, and highways, AI also helps with predictive maintenance of infrastructure. This lowers maintenance expenses and enables local officials to identify possible issues before they arise.

AI is used by environmental monitoring systems to assess pollutant levels and pinpoint environmental hazards. Cities can enhance urban sustainability and enact proactive environmental regulations thanks to these systems (Angelidou et al. 2019).

This study presents a revolutionary multi-layered framework created especially for the transformation of existing cities, in contrast to earlier smart city frameworks that mainly concentrate on freshly planned urban developments. In order to create a dynamic and adaptable system, the suggested model incorporates infrastructure systems, urban data platforms, artificial intelligence, and governance mechanisms.

Additionally, by adding well-defined sub-indicators and bidirectional feedback loops, the framework expands on previous research, allowing for a more thorough comprehension of smart city transformation processes. The topic of sustainable urban development benefits from this integrated approach's theoretical and practical contributions.

By putting forth a Culturally Responsive Smart City Transformation Model that incorporates institutional, sociocultural, and technological aspects, this study advances urban theory. This method places more emphasis on context-specific adaptation than traditional smart city models, especially in developing and post-conflict cities.

This section looks at a few foreign case studies that illustrate various strategies for smart city development and urban digital transformation in order to better understand how smart city ideas are applied in practical settings. These examples show how artificial intelligence, digital technologies, and sustainable planning techniques can be incorporated into current urban settings.

A comparative readiness evaluation was carried out to further examine the degree of smart city development across the chosen case studies. Digital infrastructure, governance systems, artificial intelligence integration, urban data platforms, transportation systems, environmental monitoring, and citizen involvement are among the important factors taken into account in the assessment. Challenges related to infrastructure modernization and data integration have also been examined in previous studies on smart cities. However, this study contributes by structuring these challenges into a multi-layered framework with clearly defined indicators and sub-indicators.

The findings show notable differences between cities that are still in the early phases of digital transformation, like Baghdad, and towns with established smart city ecosystems, like Singapore and Barcelona. The following table represents case study cities' smart city readiness evaluation.

Infrastructure, artificial intelligence, urban data systems, and governance are the four main indicators that comprise the study's findings. Through theme analysis, several sub-components were found for each of these indicators.

The results of this study demonstrate that converting current cities into smart cities is a complex process that calls for the integration of environmental management systems, governance mechanisms, technology infrastructure, and public participation. The suggested Smart City Transformation Framework demonstrates how governance systems, data platforms, digital infrastructure, and artificial intelligence must function as integrated parts rather than separate technology solutions.

The case studies examined in this study show that long-term urban planning techniques and effective institutional coordination are essential to the success of smart city programs. Intelligent urban management systems, data analytics, and sensor networks have all been incorporated into digital platforms in cities like Barcelona and Singapore. These cities provide examples of how the strategic application of urban data can enhance public service delivery, environmental monitoring, and transportation efficiency.

In a similar vein, Dubai's experience demonstrates how digital governance and e-government services facilitate effective citizen-government communication. The use of AI-supported decision-making tools and digital government platforms has greatly improved public service accessibility and administrative efficiency.

Baghdad, on the other hand, is an example of a city that is just beginning to develop into a smart city. Baghdad has fundamental issues with the coordination of governance, development of digital capabilities, and modernisation of infrastructure. Ongoing measures for digital transformation, however, point to a slow transition to technology-enabled urban management systems.

The evaluation framework is informed by ISO 37120 indicators related to urban services and quality of life.

Applications of artificial intelligence in smart cities go beyond simple data analysis. For example, adaptive signal control based on real-time traffic circumstances is made possible by the growing usage of reinforcement learning in traffic flow optimisation systems. Convolutional neural networks (CNNs) are also commonly used in environmental monitoring, especially in image-based urban analysis and air quality prediction.

Integrating contemporary IoT systems with outdated infrastructure is one of the main obstacles to modern city transformation. Technical friction brought about by this "brownfield" situation includes compatibility problems, inconsistent data, and more difficult maintenance.

Data sovereignty issues, power grid instability, and the long-term effects of post-conflict reconstruction are just a few of the intricate socio-political issues that are highlighted in the Baghdad example and have a big impact on digital adoption.

The suggested paradigm includes bidirectional feedback loops, which allow for adaptive policy-making by continuously informing governance decisions with data generated from infrastructure systems.

Simulation-based urban planning is made possible by new technologies like digital twins and GeoAI. Specifically, long-term resilience modelling for large-scale urban projects can be supported by agent-based digital twins.

Smart city technologies present sustainability issues despite their advantages, such as the high energy consumption of AI data centres and the production of electronic garbage from sensor networks.

Critical issues with algorithmic bias, data privacy, and monitoring are brought up by smart city technologies. For AI-driven urban systems to be transparent, accountable, and equitable, a "Ethics-by-Design" strategy is necessary.

By using decentralised renewable energy networks and satellite-linked IoT devices, developing cities may be able to avoid traditional infrastructure.

Large sums of money are needed to transform existing cities. Initiatives for smart cities can be greatly aided by financial tools like green bonds and Public-Private Partnerships (PPP).

The comparison of these cities indicates that institutional preparedness and governance competence are just as important to the success of smart city programs as technology investment. Because organisational capabilities and governance frameworks sometimes lag behind technical improvements, many cities find it difficult to convert technological innovation into sustainable urban outcomes.

Additionally, the use of AI in urban government presents both fresh opportunities and difficulties. Predictive urban management, traffic optimisation, environmental monitoring, and public safety can all be supported by AI-enabled technologies. However, the deployment of AI also raises concerns related to data privacy, cybersecurity, algorithmic bias, and public trust.

Another important conclusion is that social inclusion and citizen participation must be given top priority in the transition of smart cities. According to research, technology innovation by itself cannot provide sustainable urban growth unless it is bolstered by citizen participation, trust, and flexible governance frameworks.

Further institutional and financial reforms are needed in developing nations to turn existing cities into smart cities. Urban governments need to create human capital, invest in digital infrastructure, and create regulatory frameworks that can handle new technology. Smart city projects might continue to be disjointed pilot projects rather than fully connected metropolitan systems in the absence of certain structural changes.

While the governance indicator stresses the need of institutional capacity and citizen involvement, the infrastructure indicator draws attention to issues with legacy systems.

Thus, the findings of this study indicate that the transformation of smart cities should be viewed as a long-term socio-technical shift that incorporates digital technologies with sustainable urban governance and participatory planning procedures.

In the framework of sustainable urban development, this study investigated the need for and methods of converting current cities into smart cities. Digital infrastructure, urban data platforms, artificial intelligence technologies, and governance mechanisms are all included in the study's proposed integrated Smart City Transformation Framework.

The results show that implementing cutting-edge technologies alone is not enough to make the shift to smart cities. Rather, the interplay of technical innovation, institutional capability, governance systems, and public participation is necessary for successful change.

Cities that have effectively adopted smart city initiatives, like Barcelona, Singapore, and Dubai, have created integrated digital infrastructures backed by robust governance frameworks and long-term strategic planning, according to a comparative examination of worldwide case studies. On the other hand, cities like Baghdad that are in the early phases of digital transformation encounter difficulties with financial investment, institutional coordination, and infrastructure modernisation.

The study also emphasises how artificial intelligence is becoming a more important tool for data-driven urban governance. By monitoring environmental conditions, optimising transport networks, and assisting with evidence-based policy decisions, artificial intelligence (AI) technologies have the potential to increase urban efficiency. To guarantee data security, accountability, and public confidence, however, the incorporation of AI into municipal administration must be underpinned by clear legal frameworks and moral standards.

From a policy standpoint, the study recommends that governments take a comprehensive approach to the creation of smart cities, combining technical innovation with environmental management, sustainable urban planning, and citizen involvement. In order to ensure that all facets of society benefit from technological breakthroughs, future smart city policies should place a high priority on inclusive digital transformation.

Future studies should concentrate on empirical assessment of smart city transformation processes, especially in developing nations where infrastructure constraints and institutional capacity may have a major impact on the success of smart city projects. The long-term effects of artificial intelligence on social justice, sustainability, and urban governance require further investigation.

In order to facilitate the effective conversion of current cities into smart cities, several policy proposals can be put up based on the results of this study and the comparison of global smart city projects. Governance, technology infrastructure, data management, environmental sustainability, and public involvement are the main topics of these suggestions.

Policy recommendations are structured according to the four key layers identified in the proposed framework: infrastructure, data systems, artificial intelligence, and governance.

Even if our understanding of smart city development has advanced significantly, there are still many areas of turning existing cities into smart cities that need more research. Future studies should concentrate on a number of important topics that can enhance the sustainability and efficacy of smart city projects.