Mineral resources constitute the backbone of industrial and socio-economic growth, yet persistent governance weaknesses continue to limit their developmental impact in many resource-rich economies. Artificial Intelligence (AI) and Multi-Criteria Decision-Making (MCDM) offer transformative capabilities for improving decision quality, transparency, and sustainability within mineral resource management (MRM). However, systematic review of 287,601 academic records revealed zero intelligent studies simultaneously addressing mineral resource management, artificial intelligence, and regulatory policy frameworks—a tri-disciplinary void representing a critical barrier to intelligent governance. This conceptual paper proposes a unified theoretical model—the Intelligent Decision-Making System (IDMS)—which integrates regulatory governance, strategic coordination, and investment analytics through AI-enhanced MCDM techniques. Drawing on Decision Theory, Institutional Governance Theory, and Computational Intelligence Theory, the IDMS framework introduces a tri-layer structure that aligns top-down policy constraints with bottom-up data-driven optimization, forming a continuous learning system for sustainable resource governance. As a theoretical contribution, this framework provides foundations for harmonizing institutional accountability with computational efficiency and contributes to Sustainable Development Goals (SDGs) 9, 12, and 16. The paper concludes with theoretical implications, research propositions for future empirical validation, and conceptual guidelines for implementing intelligent mineral management systems.