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Power distribution and generation schemes rely heavily on predictive maintenance to keep running smoothly and efficiently. Random Forests, Decision Trees, Support Vector Machines, besides Long Short-Term Memory (LSTM) are just a few of the machine learning models that are tested in this study to see how well they do predictive maintenance in these systems. We tested these representations' ability to foretell when machinery will break down using a massive dataset consisting of one million data points gathered from a power plant and its related distribution network over the course two years. Our research shows that LSTM networks and other machine learning models greatly improve predictive maintenance. The LSTM model successfully captured temporal patterns and dependencies in the data, achieving the maximum accuracy (0.95), precision (0.94), recall (0.93), F1-score (0.94), and AUC-ROC (0.97). With an accuracy of 0.92 and an AUCROC of 0.95, the RF model likewise showed strong performance, proving that it is adept dealing with highdimensional data and non-linear connections. Decision Tree and Support Vector Machine models, on the other hand, performed about average. Important indicators for predicting equipment failures were highlighted by feature importance analysis. These indicators include vibration frequency, oil temperature, winding temperature, load current, and pressure differential. A review of the costs and benefits showed that these machine learning models, if implemented, could save around $ 1.5 million a year in maintenance and repair expenses and cut unplanned outages by about 30 %. There is a clear need for additional research to improve the generalizability of models to different power systems and to incorporate real-time monitoring adaptive maintenance techniques, while the work does show that machine learning has great potential in predictive maintenance. More dependable, efficient, and environmentally friendly power generation besides distribution systems are on the horizon thanks to the revolutionary belongings of ML on predictive maintenance, as exposed in this study.