Jack-up rigs work in challenging marine environments where conditions such as cyclic loading, corrosion, and hydrodynamic forces can quickly cause structural damage. This deterioration not only affects the safety of the operation but also shortens the rig’s service life. To address these issues, this study proposes a new way to monitor the health of these structures using artificial intelligence (AI). The approach combines the improved modal strain energy (IMSE) index with machine learning to improve how damage in the legs of the rigs is detected. Unlike traditional methods that often rely on static thresholds to identify problems, the proposed system uses AI and digital twin technology to provide more flexible and timely diagnostics, which ultimately helps with better maintenance planning and safer operations. Validation was conducted through a combination of finite element modeling and experimental testing using a 1:22 scale laboratory prototype of the SA20 jack-up rig. The results show that the AI-driven IMSE method performs better than the traditional Stubbs Index, improving accuracy by 12.5%. It also outperforms the standard IMSE method, boosting damage detection by 8.7%. Remarkably, this approach can identify damage as small as 1%, with an average deviation of <4%. On top of that, the framework has shown potential in improving multi-damage localization reliability and cutting down on false positives. This structural health monitoring (SHM) approach integrates real-time sensor data with deep learning algorithms and digital twin simulations to provide a highly scalable and adaptive solution for offshore structural integrity monitoring. The solution applies to offshore wind turbines, floating platforms, subsea pipelines, in addition to jack-up rigs, securing the long-term resilience of valuable marine infrastructure. Thus, emphasizes that AI has a truly transformational role in offshore SHM, ushering in maintenance that is intelligent, reliable, and cost-effective, especially under extreme marine conditions.