Specifically, Natural Language Processing (NLP) techniques and bibliometric analyses were combined with expert-driven evaluation to identify and refine the final corpus of high-impact studies. Through this flexible approach, the broad exploration typical of systematic reviews is retained, while a targeted curation of literature most relevant to fault detection and predictive maintenance in the mining sector is facilitated, as described in the following sections. This analysis is guided by five central research questions that address critical needs in the mining industry:  
1. Which AI- and physics-based methodologies are most effective for early fault detection in mining environments, and how do they compare in terms of performance and reliability? 2. How do digital twins and advanced monitoring strategies (e.g., IoT and Big Data) enhance the efficiency and robustness of predictive maintenance? 3. How are predictive maintenance methods being applied to improve energy efficiency and sustainability in industrial mining processes? 4. What barriers, challenges, and opportunities influence the adoption of predictive monitoring systems--particularly regarding data quality, scalability, and industrial deployment? 5. Which research gaps persist in the use of hybrid (physics- and data-driven) models and explainable AI within Mining 4.0, and how can these gaps be addressed to foster broader innovation and adoption? Beyond addressing these questions, the SLR aims to provide practical insights for both researchers and industry practitioners. By synthesizing key findings into clear, evidence-based recommendations, this review seeks to facilitate the adoption of intelligent technologies and improve the efficiency of predictive maintenance in the mining sector. Additionally, the study identifies opportunities for innovation, including the development of hybrid AI models that integrate multiple approaches to tackle mining-specific challenges and the design of adaptive frameworks for real-time monitoring in dynamic environments. Furthermore, the review emphasizes the importance of Explainable AI (XAI) models, which enhance transparency and accountability by providing interpretable fault diagnoses--a critical factor for deployment in high-risk industrial settings with stringent safety requirements [17]. From a broader perspective, bridging the gap between theoretical modeling of mechanical systems and data-driven approaches can reduce uncertainty in modeling complex phenomena such as rock fragmentation or abrasive wear [18; 19; 20]. Research on new sensor fusion algorithms [21; 22] and real-time data management technologies [23; 24] offers comprehensive solutions that integrate multi-domain data, thereby improving system reliability in large-scale mining plants [25; 26]. In the following sections, the article presents a structured analysis of the reviewed literature, providing both qualitative and quantitative insights into predictive maintenance strategies. Section 2 details the methodology employed in the SLR, outlining the inclusion and exclusion criteria, as well as the databases consulted. Following this, Section 3 provides a statistical examination of the selected articles using bibliometric techniques and visual analytics. Section 4 then presents the key findings, categorized according to the most relevant topics identified in the review. Subsequently, Section 5 interprets these results, comparing them with prior studies and highlighting critical research gaps. Finally, Section 6 synthesizes the main contributions of this work and offers recommendations for future research directions and industrial implementation.