This research explores the application of machine learning techniques for predictive maintenance. The goal is to predict potential equipment failures before they happen, allowing maintenance activities to be planned proactively. By leveraging sensor data collected from industrial machinery, including vibration, temperature, pressure, and operational parameters, machine learning models can detect hidden patterns that indicate the early stages of equipment degradation.