With the growth of the manufacturing industry, any unexpected failure of industrial equipment or machine breakdown can result in severe financial loss for the business. This is why it is critical to have a strategy for early detection and prediction for the failures. Predictive Maintenance encompasses all operational techniques and actions necessary to maintain machine availability and prevent downtime. The purpose of this article is to implement the machine-learning algorithm to develop a predictive maintenance and condition-based maintenance (CBM) systems. A real-world application of existing machine learning techniques for predictive maintenance was proposed and implemented using an artificial neural network (ANN). The results indicated that all predicted failures were correctly classified, with an overall accuracy of 99.9 percent.