Civil infrastructure, encompassing bridges, dams, and buildings, forms the backbone of modern society. Ensuring its longevity and safety is paramount, especially in the face of increasing environmental stressors and aging structures. This paper explores the transformative potential of Artificial Intelligence (AI) in revolutionizing Structural Health Monitoring (SHM). Traditional methods of infrastructure inspection and maintenance are often reactive, costly, and inefficient. By leveraging AI, we can transition to a proactive, data-driven approach. This study delves into the applications of machine learning, neural networks, and predictive analytics within SHM, emphasizing their ability to process vast datasets from diverse sensor networks. We analyze the critical role of AI in real-time anomaly detection, automated inspection, and predictive maintenance, ultimately enhancing decision-making and risk assessment. Furthermore, we discuss the integration of platforms like Proqio, which facilitate real-time data management and geotechnical monitoring, showcasing the practical implementation of AI in construction and infrastructure maintenance. The paper concludes by outlining future trends, including the development of resilient infrastructure through AI-driven predictive capabilities, highlighting the significant strides towards safer and more sustainable civil engineering practices.