To compete effectively in the global market, the mining industry faces considerable challenges in achieving desired levels of productivity and ensuring safety. Maintaining high availability of mining machinery is crucial to meet the increasing production demands. However, these machines operate in rugged and harsh environments, which can result in escalated breakdown frequency. In Indian coal mines, equipment downtime is often significant due to delays in information gathering. Implementing e-maintenance systems that utilize the Internet of Things (IoT), Artificial Intelligence (AI), and predictive analytics could effectively tackle this issue. Implementing e-maintenance facilitates real-time machine health monitoring, enabling data-driven decision-making and adopting proactive maintenance strategies. Such methodologies reduce maintenance delays, optimise maintenance schedules, and mitigate the risk associated with costly emergency repairs and unplanned downtime. Furthermore, these strategies play a significant role in enhancing equipment longevity. In this study, we proposed an e-maintenance-based strategy that utilizes the failure probabilities of machinery operated in dynamic and harsh environmental conditions and studies the effects of operating conditions on performance. A Probabilistic graphical model (Bayesian Network) was utilized for the study. The developed model has been demonstrated through a case study of an electrical motor operated in a mining dragline excavator. A GUI-based web version was designed so that users could remotely monitor the machines’ condition and plan the maintenance action needed. This study’s exciting findings will significantly enhance equipment reliability, reduce operational costs, and improve safety. By empowering proactive maintenance strategies and optimizing schedules, we can foster thorough failure analysis and minimize human error, leading to a remarkable overall productivity and efficiency boost. This ensures continuous production and extends the lifespan of critical assets, thus safeguarding both operational and financial stability.