The use of machine learning for machine failure prediction can improve operational reliability, fulfilling the goals of predictive maintenance therefore demonstrating the value of integrating machine learning towards collapse forecasting. Experts in machine learning can also look at the most important algorithms influencing workflow effectiveness. One creative use of AI software is the prediction of equipment breakdown through machine learning. It estimates equipment conditions by using predictive modelling to examine data and determine when maintenance is most needed. For industries that depend on complex sources, these solutions are essential. In this study, four classification methods were used: Random Forest (RF), k-Nearest Neighbours (k-NNs), Naïve Bayes (NB), and Decision Tree (DT). The random forest models outperformed the other prediction machine learning models by achieving 98.3% accuracy and an F1-score of 81.4%; also, the homogeneous modelling performed better than the global one with an F1-score of 87.0%. The findings demonstrated that simply keeping an eye on and updating just 9% of the network, over 73% of outages may have been avoided. The capacity of the developed models to forecast system breakdowns with the fewest false alarms is what makes them superior.