Corrosion Under Insulation (CUI) is a significant integrity concern for insulated assets, particularly in environments with thermal cycling, sweating conditions, or exposure to deluge. Its detection is hindered by insulation, making conventional inspection intervals both high-risk and low-resolution. This paper presents a framework for applying Machine Learning (ML) models to data generated by permanently installed CUI monitoring systems, particularly those based on electromagnetic guided wave reflectometry (EMGR). The approach uses machine learning to identify patterns in the data that may indicate early signs of corrosion—such as shifts in signal response or moisture detection. These insights help operators understand where and when corrosion is likely to occur, without waiting for scheduled inspections. The models can also highlight unusual changes in system behaviour that might otherwise go unnoticed. The paper also explores how this information can inform inspection planning tools, enabling site teams to prioritise high-risk areas based on up-to-date conditions rather than relying on fixed schedules. A field example from a real facility is included, illustrating how this approach enabled earlier intervention and more efficient resource utilisation. The results show how machine learning can turn monitoring data into actionable insights for safer, more targeted CUI
