The project aims to develop predictive and classification models using machine learning algorithms to analyze archaeological and chemical-physical data, offering an innovative solution for the sustainable assessment of cultural heritage. Data will be collected using advanced non-invasive diagnostic techniques. The algorithms will identify correlations between archaeological and material data, fostering new insights and predictive models useful for future studies. The project combines archaeology, chemistry, and computer science, promoting an interdisciplinary approach through collaboration between universities, companies, and cultural heritage management institutions. The objects of study include ancient paintings on stone substrates, coins, and arrowheads, as well as artifacts from sites in western Sicily (Lilibeo/Marsala, Selinunte/Castelvetrano, S. Pantaleo/Mozia, Pantelleria), the Palermo hinterland (Caltavuturo), and the central-eastern part of the island (Centuripe), housed in museums under the Department of Cultural Heritage and Sicilian Identity. The research findings will be disseminated to specialists and the community through the development of community engagement strategies. This process of cultural reappropriation will be driven by the dissemination of knowledge and the conservation and enhancement of the studied heritage, promoting recognition of the historical value of the artifacts and awareness of the interconnection between the humanities and the sciences.