Maintenance of industrial equipment is crucial for ensuring uninterrupted operations and minimizing costly downtime.Traditional reactive maintenance approaches are often inefficient and can lead to unexpected failures. In recent years, predictivemaintenance techniques powered by machine learning and data analysis have emerged as a promising solution. This researcharticle presents a comprehensive study on the development of an equipment failure prediction model for predictivemaintenance. The objective is to leverage advanced data analysis techniques and machine learning algorithms to accuratelyforecast equipment failures and enable proactive maintenance actions. The research methodology involves data preprocessing,feature engineering, algorithm selection, and model evaluation using metrics such as accuracy, precision, recall, and F1 score.Additionally, techniques for model interpretability and explanation are explored to gain insights into the factors contributing toequipment failures. The findings of this research contribute to the field of predictive maintenance by providing a robust andaccurate model for equipment failure prediction. The developed model has the potential to assist industrial organizations inimplementing proactive maintenance strategies, optimizing resource utilization, and minimizing production disruptions. Thisarticle serves as a valuable resource for researchers, practitioners, and decision-makers interested in enhancing operationalefficiency and reducing maintenance costs through predictive maintenance techniques.