Research Article

Artificial Intelligence in Mining Engineering: Opportunities, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) is emerging as a strategic enabler for modernizing mining engineering practices; however, adoption across the United States mining industry remains uneven due to operational, technical, and regulatory constraints. This paper provides a structured synthesis of peer-reviewed research, industry technical reports, government policy documents, and documented case studies to evaluate state-of-the-art AI applications across the mining value chain and to propose a practical roadmap for responsible integration within the US context. Key application domains include mineral exploration and resource modeling, mine planning and optimization, autonomous equipment operation, advanced process control, safety and risk management, predictive maintenance, and environmental compliance. Documented deployments report measurable performance gains, including 15–30% productivity improvements from autonomous haulage systems, 30–50% reductions in maintenance downtime through predictive analytics, and 1 16% throughput increases enabled by AI-enhanced process control. Despite these demonstrated benefits, widespread implementation is constrained by data quality limitations, lack of interoperability across heterogeneous equipment fleets, validation requirements for safety-critical systems, cybersecurity vulnerabilities in operational technology networks, workforce skill gaps in data science and robotics, and regulatory frameworks not yet fully adapted to autonomous and AI-driven technologies. The paper outlines a phased implementation roadmap centered on data readiness, pilot-based deployment, stakeholder engagement, cybersecurity hardening, and governance development. Future research priorities include trustworthy AI, digital twin ecosystem maturity, human–autonomy collaboration, and alignment with national objectives related to critical minerals security and sustainable mining.