Mineral resource exploration is crucial for national economic security and sustainable development. Conventional mineral prediction methods struggle with low efficiency, high costs, and insufficient accuracy in predicting concealed deep - seated deposits and those in complex tectonic areas. Recently, machine learning algorithms, with powerful data - mining and pattern - recognition capabilities, have greatly enhanced mineral prediction intelligence. This paper comprehensively sums up the research progress and development trend of machine learning in large - scale mineral resource prediction, covering algorithm frameworks like random forests, support vector machines, and deep learning, as well as data - preprocessing techniques such as feature engineering and multi - source data fusion. For instance, the random forest algorithm achieved an 82% target - area match rate in Tibet's Julong porphyry Cu - Mo deposit prediction, cutting exploration costs by about 40%; in Fujian's southwestern Makeng iron deposit study, comparing four algorithms including random forests and support vector machines, the optimal model got an AUC of 0.88. However, issues like uneven data quality and poor model interpretability remain to be solved. In the future, combining generative adversarial networks (GANs) to boost data diversity and developing a "geological constraint + AI - driven" interdisciplinary paradigm are needed to promote the intelligent and precise transformation of mineral resource exploration.