Artificial intelligence transforming minerals engineering: Key trends in literature and applications

• AI revolutionizes mineral exploration through predictive modeling. • Machine learning enhances real-time monitoring and optimization. • AI enables adaptive decision-making for efficient resource management. • Deep learning models improve mineral classification accuracy significantly. • Challenges persist in data quality, integration, and interdisciplinary expertise. Artificial Intelligence (AI) is progressively reshaping the landscape of minerals engineering, driving advancements across exploration, mining, and processing. This review systematically examines the current applications of AI in these domains, highlighting its role in optimizing resource estimation, enhancing safety, and improving operational efficiency. Through a bibliometric analysis, trends, key contributors, and geographical distributions in AI-related research within minerals engineering are explored, revealing a significant rise in AI-focused studies and a global shift towards integrating these technologies. In exploration, AI techniques such as machine learning (ML) and data analytics are utilized for mineral prospectivity mapping (MPM) and anomaly detection, facilitating more precise resource identification. In mining operations, AI aids in optimizing extraction processes, predicting equipment failures, and enabling autonomous systems for increased safety. Within mineral processing, AI contributes to real-time monitoring, process optimization, and product quality improvement through advanced modeling and control systems. Despite these advancements, challenges persist, including data quality, integration complexities, and the need for interdisciplinary expertise. This review underscores AI’s transformative impact on the sector and outlines the need for continued research and collaboration to overcome existing barriers and unlock AI’s full potential in minerals engineering.