Abstract: This paper conducts an in-depth exploration and systematic review of the current state of data processing technologies in Bridge Structural Health Monitoring (BSHM) systems, focusing on the advantages, limitations, and application scenarios of various advanced technologies, including the Internet of Things (IoT), non-contact sensing, transfer learning, Principal Component Analysis-Support Vector Machine (PCA-SVM), digital twins, and Building Information Modeling (BIM). The findings indicate that although these technologies have achieved notable progress in practical applications, they still face significant challenges, such as data quality and anomaly handling, model selection and optimization, and system integration and implementation. Looking ahead, as artificial intelligence and big data technologies continue to advance, data processing techniques in BSHM systems are expected to experience significant improvements. Specifically, the adoption of deep learning, reinforcement learning, and blockchain technologies promises to further enhance monitoring efficiency and data accuracy. Moreover, interdisciplinary collaboration will drive innovation in BSHM system technologies, providing more comprehensive and effective technical support for ensuring bridge safety and extending service life.