Structural Health Monitoring (SHM) has become a cornerstone of modern civil engineering, providing tools and technologies for the continuous assessment of infrastructure safety, reliability, and performance. Recent advancements in sensor networks, data analytics, and machine learning have transformed SHM from a reactive process into a predictive and proactive system. This paper explores the evolution of SHM, emphasizing the integration of advanced sensor technologies, wireless communication, and artificial intelligence for real-time structural analysis. The role of predictive maintenance frameworks in extending the lifespan of infrastructure and reducing costs is highlighted. Challenges in large-scale implementation, data management, and standardization are discussed, alongside future directions for building resilient, intelligent, and sustainable civil engineering systems.