Mining activities are prolific worldwide in light of the perpetual production of metal. The high need for metal materials in human life necessitates the development of mining operations, especially in places characterized as being highly enriched in metal ions. After the separation of beneficial and non-beneficial materials, industrial enrichment mechanisms take place to increase metal output. These mechanisms, known as metallurgical procedures, produce a vast volume of mining/metallurgical waste (MMW) at final disposal sites. MMW’s composition usually includes metal filings in low-pH site conditions. Thus, the environmental pollution hazard is high unless sustainable methods are implemented to reduce both heavy and toxic metals’ concentration in MMW at every disposal site. The scope of this review is to determine how cost–benefit analysis (CBA) and risk assessment (RA) could contribute positively to (a) the environmental effect of MMW reduction, (b) decreasing the environmental rehabilitation cost, and (c) research into economically sustainable methods of recovering metal from MMW.

The growing demand for Critical Raw Materials (CRMs) requires mining practices that align with sustainability and environmental, social, and governance (ESG) principles, while mining training increasingly benefits from advanced digital tools. Virtual Reality (VR) can provide high-resolution site representations that support both interactive learning and data-oriented analysis without operational risk. This study presents a VR-based framework for the quantitative assessment of pit lake rehabilitation using Virtual Excursions (VEs) developed from panoramic imagery and supported by machine-learning correction. High-resolution 360° panoramic images were used to extract geometric characteristics of a rehabilitated pit lake at the LARCO GMMSA Euboea mine site, Greece, including surface area, shoreline length, mean diameter, and maximum diameter. These image-derived estimates were validated against ground-truth data from field surveys and mine-closure documentation. To reduce systematic deviations associated with panoramic image measurements, a supervised multiple linear regression model was applied as a correction step. Validation based on Root Mean Square Error (RMSE) and the coefficient of determination ($R^2$) showed substantial improvement of the corrected estimates relative to the uncorrected image-based measurements. The results demonstrate that panoramic VR imagery can support site-specific quantitative environmental assessment in addition to its educational value. Although the present findings are limited to a single pit lake case study, the proposed workflow provides a structured basis for integrating immersive visualization, image-based measurement, and regression-based correction in post-mining rehabilitation assessment.