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Traditional maintenance approaches often result in either premature replacement of machine parts or downtime in production lines due to malfunctions. Consequently, these lead to significant amount waste in material, time and, ultimately, money. In this study, a machine learning-based predictive maintenance approach is proposed to predict the Remaining Useful Life of production lines in manufacturing. Using data collected from integrated IoT sensors in a real-world factory, we attempted to address the problem of predicting potential equipment failures on assembly-lines before they occur through machine learning models in real-time. To evaluate the effectiveness of the approach, we developed several predictive models using ML algorithms, including Random Forests (RF), XGBoost (XGB), Multilayer Perceptron (MLP) and Support Vector Regression (SVR) and compared the results for all possible variations …