The transition toward electrified and digitally connected mineral production creates an opportunity to manage mining performance as a coupled engineering, safety, ecological, and financial system rather than as a set of independent functions. This paper evaluates an AI-enabled smart-mining framework using real-life summary results supplied for an anonymized critical-mineral mining case. The framework integrates mine electrification, predictive maintenance, occupational and process safety, biodiversity monitoring, and nature-related financial accounting. Five data domains are examined within a common decision architecture: electrical and production performance, equipment reliability, safety risk analytics, environmental biology, and accounting and liability outcomes. The reported case compares a conventional operating baseline with a smart-mining condition and evaluates seven AI safety-risk models, including logistic regression, support vector machine, artificial neural network, random forest, long short-term memory, XGBoost, and a multi-source fusion ensemble. The real-life results show a 22.0% reduction in energy use per tonne, a 22.1% reduction in haulage CO₂-equivalent intensity, a 32.9% reduction in haulage operating cost, and a 37.9% reduction in electrical fault events. The multi-source fusion ensemble records an F1-score of 96.3% and ROC-AUC of 0.985. Ecological rehabilitation increases the Biodiversity and Restoration Index from 42.6 to 74.0, while the : case-level financial analysis reports a recurring annual benefit of US$16.2 million, a simple payback of approximately 3.0 years, and a seven-year NPV of approximately US$30.9 million at 10%. The overall Smart Sustainable Mining Performance Index increases from 58.2 to 83.8, while the Integrated Operational Risk Score decreases from 65.7 to 31.4. The results demonstrate how electrical performance, equipment condition, worker safety, ecological recovery, and financial reporting can be evaluated as interdependent components of smart critical-mineral mining.

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