A Systematic Review of AI in Cultural Heritage Preservation: Technological Frameworks, Applications, and Future Directions

Cultural heritage faces threats from deterioration, environmental factors, and fading traditions. While traditional preservation methods struggle with efficiency, artificial intelligence enables a data-driven transformation. This review systematically examines AI applications in heritage conservation. We propose a framework organizing AI’s role into three areas: Restoration (from damage diagnosis to digital reconstruction), Understanding (from visual analysis to semantic interpretation), and Management (from object monitoring to systemic oversight). The study highlights advances in image enhancement, digital repair, and heritage modeling across different heritage types. We identify key challenges in data, technology, and ethics, and outline six future directions: multimodal AI, explainable systems, safe generative restoration, cultural consistency methods, heritage large models, and digital twins. This survey provides a clear research roadmap for interdisciplinary work connecting computer science with cultural preservation.