## 5 Discussion
Moreover, it outlines the primary challenges limiting broader adoption, such as data integration constraints and computational scalability, alongside potential solutions that leverage cloud computing, decentralized data frameworks, and advanced optimization models. Similarly, Table 4 provides a domain-specific perspective by detailing the predominant sensors and machine learning techniques employed across various stages of hydrometallurgical processing. This table summarizes, for each process stage, the key types of sensors utilized--including pH, ORP, temperature, flow, vibration, and energy consumption sensors--and the corresponding machine learning techniques applied, such as Random Forest, Convolutional Neural Networks (CNNs), and Reinforcement Learning. The integration of IoT sensors with these AI algorithms has facilitated real-time monitoring and predictive analytics, leading to significant improvements in operational resilience and sustainability. By leveraging data from multiple sources, these methodologies enable improved predictive maintenance strategies, process optimization, and fault reduction. The references included in the tables further validate the applicability of these approaches in complex industrial settings, demonstrating the effectiveness of machine learning models in various mining operations. The structured insights presented reinforce the importance of a multidisciplinary approach in developing next-generation predictive maintenance frameworks. The convergence of digital twins, hybrid AI models, and secure data architectures presents a compelling pathway toward achieving autonomous, self-adaptive maintenance strategies in the mining sector. However, overcoming interoperability constraints, establishing standardized data protocols, and scaling real-world implementations remain critical avenues for future research. By addressing these gaps, predictive maintenance can transition from an isolated optimization tool to a fully integrated component of Mining 4.0, ensuring long-term sustainability and operational excellence. Finally, an important point to address in this discussion is the methodology used in our systematic review. Despite the rigorous approach taken, several limitations should be mentioned. First, our literature search focused only on the Scopus and Web of Science databases. While these sources are extensive and widely accepted, relevant studies from other respected databases may have been overlooked. Additionally, limiting the review to articles published in English introduces possible language bias, as valuable findings published in other languages could have been missed. Another limitation relates to our semi-automated thematic analysis, which relied on NLP-based clustering and classification. Although this method is scalable, objective, and consistent, it can lead to classification errors or miss subtle themes more easily identified by human reviewers. Lastly, publication bias may also affect our findings, since studies reporting negative or inconclusive results are less likely to be published. Acknowledging these limitations ensures transparency and critical reflection. Future research could overcome these issues by incorporating additional databases, expanding language inclusion criteria, and using manual checks alongside automated methods.