AI-driven Mining 4.0: A Systematic Review of Smart, Sustainable, and Autonomous Technologies across the Mining Lifecycle
Maaz A. Ali *
Geological Research Authority of Sudan, Ministry of Minerals, Khartoum, Sudan and Saudi Mining Polytechnic (SMP), Arar, Saudi Arabia.
Mohammed Izeldeen Mohammed Ali
Geological Research Authority of Sudan, Ministry of Minerals, Khartoum, Sudan.
Abazr A. Osman
Geological Research Authority of Sudan, Ministry of Minerals, Khartoum, Sudan.
*Author to whom correspondence should be addressed.
Abstract
The mining industry is undergoing a profound transformation driven by the integration of advanced technologies across the entire mining lifecycle. This systematic review presents a comprehensive analysis of key innovations from early-stage exploration to post-closure environmental management. It examines the role of Artificial Intelligence (AI), machine learning, digital twin systems, autonomous equipment, sensor-based ore sorting, bioleaching, and IoT-enabled environmental monitoring within the context of Mining 4.0. By synthesizing peer-reviewed literature and recent industrial case studies, the paper highlights how these technologies improve efficiency, safety, and sustainability across each stage of the mining value chain.
While the potential of these technologies is considerable, the review also acknowledges common implementation challenges such as high capital costs, regulatory barriers, and digital skills gaps. Ultimately, this study offers an integrated perspective on how technological advancement is reshaping the mining sector, providing insights for researchers, industry practitioners, and policymakers committed to building the mines of the future.
Keywords: Artificial Intelligence (AI) in mining, mining 4.0, digital twin, autonomous mining systems, sensor-based ore sorting, sustainable mining, mineral exploration innovation, predictive maintenance, IoT in mining, digital transformation, mine closure, ESG in mining