Integration of Artificial Intelligence (AI) and Internet of Things (IoT) for Hazard Detection and Accident Prevention in Mining

Mining is among the world’s most hazardous industries, where workers face constant exposure to risks such as rock falls, gas explosions, and equipment accidents. Despite advances in mechanization and safety management, fatal incidents persist due to the limitations of traditional monitoring and reactive safety systems. The advent of Artificial Intelligence (AI) and Internet of Things (IoT) has introduced new opportunities to enhance predictive, automated, and real-time safety solutions. This systematic review synthesizes current research on the integration of these technologies for hazard detection and accident prevention in mining. The study categorizes applications across geotechnical, environmental, and operational hazards and examines their technical mechanisms, benefits, and limitations. Findings reveal that AI enables data-driven hazard prediction; IoT ensures real-time environmental and equipment monitoring; and robotics extends operational safety through autonomous inspection and intervention. However, challenges including data scarcity, connectivity issues, and lack of standardization limit large-scale deployment. The review highlights future research directions such as digital twin development, edge computing, explainable AI, and human–robot collaboration as pathways toward intelligent, ethical, and sustainable mine safety systems.