Potentially toxic elements (PTEs), a term used here for metals and metalloids commonly grouped as “PTEs” in environmental literature, occupy a dual position in mining landscapes as economically valuable components of ore systems and as potentially persistent contaminants released during extraction, processing, waste storage, and post-closure weathering. Geological exploration and environmental assessment draw on a partially overlapping geospatial and geological evidence base, but they address distinct prediction targets and causal processes. This review examines how artificial intelligence, geographic information systems, remote sensing, unmanned aerial vehicles, geochemistry, and three-dimensional geoscience data can be integrated across the mine life cycle without conflating mineral prospectivity, elemental concentration, contamination attribution, exposure, toxicity, and environmental risk. A critical narrative review of recent peer-reviewed literature, complemented by foundational studies in mineral prospectivity mapping, environmental geochemistry, and spatial uncertainty, was used to synthesize advances in machine learning, deep self-attention, semi-supervised learning, geographically weighted algorithms, explainable artificial intelligence, Bayesian approaches, and Dempster-Shafer evidence fusion. The evidence indicates that modern models can improve spatial prediction when multi-source geological and environmental data are integrated, but predictive accuracy alone is insufficient for responsible decision-making. Major limitations arise from sparse and preferential sampling, censored and compositional geochemical data, uncertain negative labels, scale mismatch, spatial autocorrelation, workflow-induced uncertainty, limited transferability, sensor-dependent indirectness, and inadequate separation of geogenic enrichment from mining-derived contamination. The review proposes an integrated GeoAI framework in which exploration targeting, baseline characterization, operational monitoring, tailings surveillance, contamination mapping, and closure planning share a common data architecture while retaining task-specific targets, validation designs, and uncertainty products. Two original synthesis figures clarify the partially overlapping evidence architecture and summarize representative published case studies. This exploration-to-environment perspective has the potential to reduce duplicated sampling, improve early recognition of future contamination pathways, support risk-based monitoring, and strengthen the traceability of sustainable mining decisions.