Asian Journal of Current Research

Critical minerals underpin energy, defence, digital and advanced-manufacturing systems, yet the economic value of a deposit depends not only on in situ grade but also on how much mineral value can be recovered under variable geological, mineralogical and operating conditions. In the United States, prediction is particularly difficult because critical-mineral resources span carbonatites, peralkaline rhyolites, lithium-bearing caldera sediments, polymetallic hydrothermal systems and other deposit classes with distinct spatial architectures and processing responses. This critical narrative review evaluates geostatistical and machine-learning methodologies for predicting recoverable mineral value across regional prospectivity, three-dimensional resource models, geometallurgical domains and mineral-processing operations. Literature was selected through live searches of accessible scholarly indexes, bibliographic registries, institutional repositories and official scientific sources, with an end date of 2 June 2026. The synthesis shows that geostatistics remains indispensable for modelling spatial continuity, sample support, geological domains and uncertainty realisations, whereas machine learning is most useful for nonlinear integration of geochemistry, geophysics, hyperspectral mineralogy, imagery and process-sensor data. Neither family is sufficient alone. Kriging can smooth extreme grades and does not automatically represent recovery behaviour; machine-learning models can exploit spatial or temporal leakage, obscure extrapolation and deliver overconfident predictions when training data are sparse or preferentially sampled. The strongest methodological direction is therefore hybrid and scale-aware: geological domaining and support harmonisation should precede modelling; geostatistical simulations should represent plausible orebody variability; machine learning should estimate nonlinear grade, mineralogical or recovery responses; and uncertainty should be propagated to recoverable metal, concentrate quality, throughput and economic decisions. U.S. applications are currently constrained by proprietary metallurgical data, inconsistent sampling, weak external validation and limited public benchmarks. Progress requires paired geological-mineralogical-metallurgical datasets, spatial and temporal validation, calibrated uncertainty, decision-centred performance metrics and transparent model governance. An integrated workflow is proposed to connect national mineral-potential mapping with block-scale geometallurgy and operational recovery control without conflating occurrence, grade and metallurgical recovery.