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Scopus and Web of Science (2018\u20132025) were searched using replicable queries, and a dual text\u2010representation pipeline (TF\u2013IDF with bi\/trigrams and sentence\u2010transformer embeddings) was applied. Model selection scanned\n                    <jats:italic>k<\/jats:italic>\n                    over a predefined grid with internal indices (Silhouette, Davies\u2013Bouldin, and Calinski\u2013Harabasz), and robustness was assessed through multiseed stability, bootstrap consensus, representation\u2010sensitivity checks, and a control run with HDBSCAN. Study quality and risk of bias were appraised with an AI\u2010and\u2010control\u2013oriented matrix (ACE\u2010QA). Two macroclusters emerged. The first centers on distributed control, consensus and formation, fault tolerance, observers, and learning\u2010based designs (fuzzy\/neural\/RL), including finite\/predefined\u2010time and event\/dynamic event\u2013triggered mechanisms. The second addresses secure and resilient cooperation under cyber threats (DoS, deception, and FDIA), integrating observer\u2010based estimation and communication\u2010efficient protocols. Cross\u2010cutting findings indicate that event\u2010triggered updates reduce bandwidth and compute requirements, while robust estimation and fault\u2010tolerant control improve availability under harsh conditions and intermittent networks\u2014typical in mining. A maturity map suggests high technical readiness and growing adoption for RNN\u2010based sensing analytics, advancing readiness but emerging adoption for multiagent coordination, and early adoption of LLMs for text\u2010grounded maintenance intelligence. Evidence gaps persist in replicability, cross\u2010site transfer, uncertainty reporting, and mining\u2010grade validation at the edge. A design agenda is outlined that prioritizes digital\u2010twin stress testing, edge\u2010first evaluation of agent coordination, secure\u2010by\u2010design pipelines (authenticated\/encrypted messaging and adversarial testing), and shift\u2010aware validation. In sum, a hybrid stack\u2014RNNs for perception, LLMs for knowledge grounding, and agents for coordinated action\u2014offers a practical route to reliable, secure, and communication\u2010efficient predictive maintenance in Mining 4.0.\n                  <\/jats:p>","DOI":"10.1155\/int\/9953223","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T07:05:01Z","timestamp":1762931101000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Systematic Review of Intelligent Agents, Language Models, and Recurrent Neural Networks in Industrial Maintenance: Driving Value Creation for the Mining 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