<p>Structural health monitoring (SHM) systems are widely applied in buildings and bridges to assess structural integrity, detect cracks, and monitor environmental conditions, loads, and responses. A major challenge lies in developing an integrated framework that ensures efficient data collection, processing, and interpretation. SHM systems rely on multiple sensors that produce large volumes of data, requiring advanced processing techniques to convert raw signals into actionable insights for inspection, maintenance, and management. Traditional analysis methods often struggle with environmental noise, data complexity, and scalability. Recent advances in computing and imaging technologies have enabled the use of machine learning (ML) for efficient SHM data processing. Among these, the You Only Look Once (YOLO) algorithm stands out for its real-time object detection capability, striking a balance between accuracy and computational speed—ideal for identifying cracks and structural defects in complex environments. This review categorizes state-of-the-art ML algorithms used in SHM, with a focus on their role in damage classification and structural assessment. It highlights the importance of combining physical models, robust feature extraction, uncertainty handling, and parameter estimation to improve the accuracy and reliability of SHM systems. Special attention is given to YOLO-based frameworks for their superior performance in real-time damage detection and monitoring. Overall, the integration of advanced ML, particularly YOLO, into SHM systems marks a significant step toward more accurate, scalable, and cost-effective solutions for long-term infrastructure monitoring and maintenance.</p>