<p>The preservation of cultural heritage structures is essential for safeguarding the legacies of past civilizations. Traditionally, conservation relied on manual inspections and expert evaluations, but Artificial Intelligence (AI) and Machine Learning (ML) are transforming these methods. This review synthesizes the role of AI and ML in heritage conservation, focusing on structural analysis, damage detection, monitoring, and restoration. A key gap in existing reviews is the lack of a comprehensive framework for AI applications in heritage preservation. While individual AI techniques have been explored, no unified approach exists. This review addresses this gap by proposing a novel classification framework, categorizing AI-driven methodologies into computer vision, deep learning, structural health monitoring (SHM), and predictive modeling. Systematic searches across Google Scholar, IEEE Xplore, and Scopus were conducted using keywords such as “AI in heritage conservation,” “machine learning for structural health,” and “cultural heritage preservation.” Peer-reviewed articles published from 1965 to 2025 were included, excluding studies unrelated to civil infrastructure or lacking empirical case studies. Over 100 + literature reviews and 50 + case studies were examined. Key findings indicate that AI-based methods Like CNNs and GANs are effective for damage detection and material degradation prediction. Tools such as Agisoft Metashape and Meshroom enhance 3D scanning for heritage sites, while TensorFlow and Keras help extract preservation insights from images and scans. Challenges, including data scarcity and ethical concerns, remain. The review concludes with recommendations for open data collaboration and the development of explainable AI models to improve global heritage preservation.</p>