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The global demand for metals and construction materials necessitates faster and more precise exploration strategies, especially for deep-seated mineralization and in unexplored areas known as Greenfield. Traditional methods based on direct evidence are often insufficient, highlighting the need for advanced techniques. Mineral Prospectivity Mapping (MPM) is a crucial process that integrates various geoscientific datasets, such as geological, geophysical, and geochemical data, within a geospatial domain to create predictive models for identifying potential ore zones. This process is essentially a multiple criterion decision making (MCDM) task aimed at categorizing areas based on their likelihood of containing mineralization. Artificial Intelligence (AI) and machine learning techniques are significantly enhancing MPM by providing powerful data integration and analysis capabilities. The sources discuss different AI approaches, including knowledge-driven methods like Fuzzy Inference Systems (FIS), which utilize techniques such as the Fuzzy Gamma Operator and Multiclass Index Overlay. These are considered well-suited for certain tasks, particularly in Greenfield exploration or when labeled data is scarce. Unsupervised anomaly detection algorithms, such as Isolation Forest (IF) and Extended Isolation Forest (EIF), are also proving effective in MPM by revealing complex, nonlinear patterns associated with hidden mineralization zones without requiring labeled training data. Integrated AI approaches hold significant promise for optimizing resources and accelerating the discovery of new mineral deposits. P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly Valls Geoconsultant ORCID ID- https://orcid.org/0000-0002-5421-0914 Scopus Author ID: 7003369619/35335510700 ResearcherID: S-6604-2018 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets