tion, improved safety protocols, and a reduction in downtime. This proactive approach is becoming essential for maintaining competitiveness in the global market [1; 2; 3; 4]. To address these challenges, intelligent technologies have emerged as key enablers of predictive asset management in the mining industry. Advances in artificial intelligence, deep learning algorithms, and next-generation sensors have significantly improved the ability to process vast amounts of operational data. These technologies are particularly effective in identifying anomalous behavior patterns, allowing operators to detect potential failures before they occur. As a result, predictive maintenance strategies are becoming more sophisticated, helping to optimize asset lifecycles, reduce operational expenses, and minimize unexpected downtime. However, despite their potential, the implementation of these technologies in mining remains complex. Highly variable operational conditions, the diversity of asset types, and the technical limitations of current predictive models present significant barriers to large-scale adoption. Overcoming these challenges requires a structured, systematic evaluation of existing methodologies to refine predictive techniques, establish best practices, and ensure the seamless integration of AI-driven maintenance solutions in mining operations [5; 6]. Emerging research in advanced mathematical modeling emphasizes new approaches to represent harsh conditions in open-pit mines [7; 8], demonstrating the complexity of capturing nonstationary behaviors. Integrating these mathematical frameworks with AI-based analytics is challenging but essential [9; 10], as it paves the way for more accurate fault detection and longer equipment lifespans in mining. Despite these promising developments, several critical research gaps persist in the domain of predictive maintenance for mining. Chief among them is the scarcity of robust, standardized datasets from diverse operational settings, which limits the validation and scalability of existing algorithms. Additionally, many proposed methods remain confined to controlled or simulation-based studies, lacking large-scale industrial demonstrations under harsh environmental conditions. Interoperability challenges, arising from heterogeneous sensor technologies and incompatible data formats, further hinder the seamless adoption of predictive analytics across different stages of mining operations. Consequently, there is a pressing need for more comprehensive, real-world evaluations and collaborative frameworks that bridge these gaps, ensuring that advanced monitoring solutions can be effectively implemented and sustained in practice. In order to address this gap, this study conducts a Systematic Literature Review (SLR) to analyze recent advancements in AI-driven predictive monitoring for mining applications. By systematically reviewing 166 high-impact studies from Scopus and Web of Science, this research identifies the most effective methodologies for fault detection, predictive modeling, and intelligent asset management in extreme environmental conditions. The review also explores key trends, including the growing adoption of hybrid AI models, digital twins, and IoT-enhanced monitoring, while highlighting critical research challenges such as data standardization, model scalability, and real-time implementation [11; 12; 13]. By synthesizing these contributions, this study provides a structured framework for advancing predictive maintenance strategies in the mining industry, offering practical insights to guide future research and industrial applications. However, the lack of standardized data repositories continues to hinder model validation under real-world industrial conditions, evidencing the gap between theoretical advancements and practical implementation [14; 15; 16]. Given the extensive and heterogeneous nature of the mining domain, a semi-automated search and filtering strategy was adopted instead of relying exclusively on a traditional PRISMA framework.