The global drive to reduce carbon emissions and the footprint of the construction industry and its effect on climate change has led to construction organisations integrating life cycle carbon assessment into their activities. One of the key areas for enhancing sustainability is the reduction of embodied carbon in the materials. This comprises the carbon associated with intrinsic features of the materials themselves, transportation, and installation-related emissions. There is also the carbon emission associated with the maintenance, and operation of the built assets during their life cycles. To enable the rapid evaluation of the whole-life carbon, i.e. carbon emission during design, construction, operational and end-of-life phase of buildings, this study presents a novel approach to integrating artificial intelligence (AI) into existing practices with focus on embodied carbon. This novel approach comprises the integration of data using AI to provide quick guidance for designers during the early design stage of the process to reduce the life-cycle carbon impact. This approach will exploit a wide range of datasets from up-to-date Carbon databases and previous construction projects and their carbon footprint. This will allow a reasonable estimation of the carbon footprint of different design choices for clients and designers, allowing them to collaborate rapidly reaching an optimum configuration and material selection for their buildings. Current practice involves manual input of carbon data into carbon estimation software to enable the output. With the integration of fuzzy logic and deep-learning neural networks, the new proposed process will contribute to time savings and enhanced decision-making. The paper begins with a literature review of the importance of monitoring carbon emissions in the construction industry, followed by a discussion of CarboniCa Software, an in-house carbon assessment package used by a major UK construction organisation. And finally, an analysis of how AI capability will support the software for rapid evaluation of whole life carbon will be presented. This application highlights the importance of AI towards a more efficient and sustainable future.