## 6 Conclusions and Future Perspectives  
The body of research discussed in this review underscores that predictive maintenance has evolved into a multi-faceted discipline interweaving machine learning, digital twins, IoT architectures, and hybrid physics-informed models to tackle the ever-increasing operational demands in harsh mining environments. Studies integrating these techniques have consistently achieved significant reductions in unplanned downtimes, optimized energy consumption, and fostered safer working conditions [8, 10, 11, 51, 52, 53, 62]. In tandem, hybrid AI approaches--combining data-driven analytics with physical models--demonstrate strong adaptability for varying load states and unknown disturbances, ensuring higher reliability even under extreme mining conditions [12, 13, 36, 74]. Additionally, our systematic literature review findings reveal that these data-driven strategies can substantially boost operational performance by detecting faults at an earlier stage, minimizing process interruptions, and reducing accidents. In particular, the incorporation of advanced fault diagnosis methods in grinding mills, conveyors, and ventilation \begin{table}
\begin{tabular}{c c c c} \hline
**Model/Approach** & **Data Needs** & **Computational Load** & **Interpretability/Cost** \\ \hline Method A (Shallow ML) & Low to Medium & Moderate (edge-friendly) & Medium interpretability/Low cost \\ Method B (Deep Neural) & High & High GPU/Cloud & Lower interpretability/Higher cost \\ Method C (Hybrid Domain + ML) & Medium & Varies & Potentially high interpretability/ \\ \hline \end{tabular}
\end{table} 
Table 6: Recommended comparative table of mining-oriented predictive models. systems has been instrumental in improving equipment longevity and worker safety. This progress aligns with the research queries posed, confirming that predictive maintenance, fortified by AI technologies, can deliver tangible benefits in both cost reduction and risk mitigation across diverse mining operations. From an _energy efficiency_ perspective, the literature highlights how advanced fault detection algorithms and adaptive controllers can minimize idle running of heavy machinery and avoid peak power draws [43; 44; 54; 63]. Multi-objective optimization frameworks have emerged as pivotal in achieving a dual objective of process throughput and resource preservation [4; 13; 53]. Meanwhile, the _digital twin_ paradigm introduces real-time, high-fidelity system replicas that not only preempt catastrophic failures but also refine strategic maintenance planning via what-if analyses [58; 64; 66; 80]. These approaches are further strengthened by the integration of _IoT sensor networks_, allowing for continuous data collection and predictive analytics, coupled with secure _data traceability_ through emerging distributed ledger technologies [40; 41; 42; 43; 44]. Despite these advancements, several _research gaps_ remain. First, _standardized and openly available datasets_ tailored to harsh mining environments are scarce; this lack of consistent data hinders both reproducibility and large-scale benchmarking of emerging methods [39; 40; 41; 89]. Second, the _interpretability_ of complex deep learning models remains a challenge for on-site technicians, underscoring the necessity of _explainable AI_ approaches to bridge the gap between automated predictions and actionable maintenance decisions [32; 38; 56]. Third, the high _cost of deployment_ for extensive sensor networks and robust digital twin platforms poses a barrier, particularly for small- and medium-scale mining operations. Developing scalable, federated approaches that distribute computational loads and reduce upfront capital investment remains an open challenge [19; 20; 37; 61; 86]. Moreover, the review highlights the crucial role of pilot projects and case studies to validate these predictive maintenance frameworks in real-world scenarios.