In today’s era, where the resilience and safety of infrastructure are more critical than ever, the integration of machine learning into the field of structural health monitoring (SHM) marks a fundamental shift toward a future where preventive maintenance and proactive risk management become not only possible but also more efficient and reliable. This study fits into this innovative context, proposing a state-of-the-art approach for structural monitoring that harnesses the potential of machine learning for more accurate and faster damage detection and classification. The developed architecture based on a four-layer model integrates machine learning algorithms with advanced sensor technologies and Internet of Things (IoT)-based systems to create a complex yet intuitive monitoring system. Autoencoders and the k-Nearest Neighbor (kNN) algorithm allow the identification of anomalies and classification of the damage typology. The experimental phase, conducted on a developed prototype, confirms the effectiveness of the applied machine learning techniques, with significant accuracy in damage detection and classification. This study contributes to the structural engineering sector by introducing more advanced and reliable methods for safeguarding infrastructure.