Structural Health Monitoring (SHM) is essential for the maintenance and management of modern infrastructure, ensuring safety, longevity, and optimal performance. However, traditional SHM techniques often face limitations regarding accuracy, real-time responsiveness, scalability, and adaptability. In recent years; Artificial Intelligence (AI) has significantly enhanced SHM capabilities, revolutionizing data analysis, damage detection, and predictive maintenance. AI-based approaches—particularly machine learning and deep learning algorithms—offer robust solutions for automatic feature extraction, anomaly detection, and fault prediction, enabling real-time analysis and proactive decision-making. Architectures; such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) facilitate precise and automated detection of structural anomalies using data from sensors, images, and videos. Moreover; AI-driven methods effectively handle sensor faults, perform data reconstruction, and estimate remaining useful life (RUL), contributing to improving system reliability and performance. Emerging technologies; including digital twins, edge computing, and UAV-based inspections, further strengthen AI integration within SHM, paving the way for intelligent infrastructure systems. Nonetheless; challenges related to data quality, model generalization, computational limitations, and interpretability remain, underscoring key areas for future research and development.