• vamshikrishna challa
• Sudhir Sitaram Amritphale
• J. S. Chouhan

Critical minerals are essential to electrification, renewable energy, advanced electronics, defense systems, and modern manufacturing, but their supply chains are increasingly strained by declining ore grades, geographic concentration, environmental constraints, and the growing volume of complex secondary resources. Artificial intelligence (AI) and machine learning (ML) are being applied across mineral beneficiation, flotation, hydrometallurgy, rare-earth separation, bioleaching, mine-waste recovery, and battery recycling, yet results from these areas are seldom judged using a common standard. This review brings these strands together and examines where AI is already providing credible engineering value, where the evidence is still preliminary, and what is needed for dependable scale-up. Across the literature, ensemble trees, support-vector methods, neural networks, computer vision, and hybrid physics-ML approaches have been used for recovery prediction, soft sensing, operating-condition optimization, froth analysis, leaching design, and rare-earth solvent-extraction screening. The strongest studies go beyond random train/test splits and include external, experimental, or prospective validation. Common limitations include small or proprietary datasets, changing feed characteristics, model drift, limited treatment of uncertainty, weak transferability between ores or waste streams, and insufficient connection to metallurgical fundamentals. On this basis, we propose two practical tools: a nine-test benchmarking framework for judging whether a recovery model has enough validation evidence for pilot or plant use, and a seven-layer architecture that places AI between resource characterization and process decision-making. We further argue that optimization should consider recovery and purity together with energy, water, reagents, emissions, residues, and cost. In this context, AI is most useful as a support layer for adaptive and circular critical-mineral recovery rather than as a substitute for process science.

Artificial intelligence; critical minerals; mineral processing; hydrometallurgy; bioleaching; battery recycling

"Artificial Intelligence in Critical Mineral Recovery: From Beneficiation to Recycling, and a Framework for Validating Deployment-Ready Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.d308-d327, September-2026, Available :http://www.jetir.org/papers/JETIR2609332.pdf

"Artificial Intelligence in Critical Mineral Recovery: From Beneficiation to Recycling, and a Framework for Validating Deployment-Ready Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppd308-d327, September-2026, Available at : http://www.jetir.org/papers/JETIR2609332.pdf