73 Accordingly, the results are best understood as hypothesis-generating maps that require subsequent geological validation and refinement at district and deposit scale. A further strategic extension of this framework involves the incorporation of socio-environmental and governance constraints as external decision layers applied after geological prospectivity modeling. While such variables do not form part of the Mineral System itself, they strongly influence exploration feasibility under real-world conditions. Overlaying high-permissivity geological domains with datasets representing mining conflicts, land-use restrictions, protected areas, or regulatory frameworks would allow differentiation between geological potential and operational viability. This two-stage approach preserves the integrity of the process-based Mineral Systems model while expanding its applicability toward strategic exploration planning. In regions such as southern Peru, where social license and land access represent critical exploration risks, integrating these constraints may significantly refine target prioritization and capital allocation decisions. The proposed framework should be understood as an advanced regional-scale prototype designed to support early-stage exploration targeting rather than a fully operational predictive system. While the model demonstrates internal consistency by reproducing known porphyry–skarn districts and identifying additional permissive domains, its effectiveness remains dependent on dataset resolution, proxy selection, and spatial scale. As such, the current implementation provides a robust proof-of-concept for integrating Mineral Systems with machine learning. Further validation using higher-resolution datasets and independent testing areas is required before it can be considered a decision-critical exploration tool. 4. CONCLUSIONS The Random Forest feature importance analysis indicates that Fertility is the primary permissive factor controlling regional prospectivity in the Andahuaylas–Yauri Batholith province. This is consistent with the Mineral Systems framework, where fertile magmatism represents a first-order requirement for porphyry– skarn formation at regional scale. Prospectivity maps generated by both Random Forest and Neural Networks delineate highpermissivity domains spatially associated with the batholith. Several of these domains coincide with known porphyry–skarn deposits, supporting the geological coherence of the approach. In addition, new permissive areas were identified outside historically explored corridors, highlighting zones that warrant further investigation. The integration of the Mineral Systems approach with supervised machine learning provides a transparent and reproducible tool for regional-scale exploration targeting. However, results are dependent on dataset quality, proxy selection, and spatial scale, and should therefore be interpreted as relative indicators of permissivity rather than deterministic predictions. Incorporating higher-resolution geophysical, geochemical, and radiometric datasets would improve model refinement and reduce geological uncertainty in future applications. 5. RECOMMENDATIONS Advancing regional exploration in southern Peru requires moving beyond permissivity mapping toward a more discriminative understanding of mineral system efficiency. Future models should prioritize quantitative fertility indicators capable of distinguishing productive magmatic suites from barren intrusions. Geochemical ratios such as Sr/Y, La/Yb, Eu/Eu*, trace-element systematics, and zircon-based fertility proxies represent critical next-generation inputs that would substantially refine current lithology-based representations of magmatic potential. Equally important is the improvement of crustal architecture characterization. Integrating regional geophysical datasets and more precise structural interpretations would allow a transition from simplified buffer-based structural influence models to physically constrained representations of magma ascent corridors
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