Integration of Artificial Intelligence into SHM is a tectonic shift in infrastructure management, bringing unprecedented advances in safety, reliability, and operational efficiency. AI-driven SHM utilizes machine learning, deep learning, and data-driven analytics methodologies for new ways of detecting, assessing, and predicting the performance of structures and structural anomalies. Some recent developments are demonstrating how AI will process a large amount of sensor data in near real time, extracting and analyzing with unprecedented accuracy from damage detection to anomaly identification through to predictive maintenance. Regarding SHM, machine-learning algorithms like support vector machines and random forests are commonly employed to perform feature extraction and classification tasks. Deep learning techniques, including CNN and RNN methods, are particularly effective when addressing complex high- dimensional data related to various types of structural systems, from bridges to high-rise buildings and even offshore platforms. Inspite of these, challenges include the availability of high-quality labeled data, since AI models depend hugely on training data for performance, and interpretability problems of these models, which may act as a barrier for their adoption in critical infrastructure monitoring. Besides, integrating the solutions with existing SHM frameworks also requires seamless communication among different hardware and software systems that often necessitates major technological upgrades. Standardized dataset development, interpretable AI algorithms, and robust integration protocols call for future research. In that way, it will be very useful if the collaboration of researchers, industry practitioners, and policymakers bring the full potential of AI. The continuous maturation of AI is sure to continue its application in SHM and thereby help develop a host of safe, resilient, and sustainable infrastructure systems as one of the key future developments in structural engineering.