This work develops and validates an AI-driven, non-destructive methodology for assessing the structural condition of masonry elements. It combines computer vision techniques with machine learning and deep learning models to automatically detect, classify, and segment cracks in masonry surfaces. The approach aims to support scalable, data-driven inspection workflows that improve accuracy, reduce manual effort, and enable informed decision-making in circular construction contexts.