Machine and Deep Learning Techniques for Critical Mineral Exploration: A Review of International Research Trends and Applications by Deposit Type

Driven by carbon neutrality policies and energy transitions, the demand for critical minerals, including lithium, cobalt, nickel, copper, graphite, and rare earth elements, is rising rapidly, and exploration has shifted toward concealed deposits beneath cover sequences.Currently, machine and deep learning complement conventional geological, geochemical, geophysical, and remote sensing techniques.This review synthesizes a decade of research on AI-based mineral exploration across five dimensions: theoretical foundations, data modality applications, mineral prospectivity mapping, deposit-type-specific applications, and emerging trends.DL architectures, such as CNNs, autoencoders, GANs, transformers, and graph neural networks, exhibit strong performance in pattern recognition, data fusion, and workflow automation.Emerging paradigms, including explainable AI, physics-informed neural networks, selfsupervised learning, and foundation models, address data scarcity and black-box challenges.Different strategies have emerged for porphyry Cu-Mo, orogenic gold, sediment-hosted Zn-Pb, rare-earth elements, and lithium pegmatites.AI-based exploration is expected to play a decisive role in discovering concealed deposits and stabilizing critical mineral supply chains.