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Abstract: Gear box is crucial in industrial processes, enabling adjustments of speed and load conditions to meetoperational needs. As gear box technology advances, their capabilities increase, but component failure can result in product losses and maintenance costs. Detecting potential failures before hand is essential, and vibration measurement is a proven method for monitoring machine condition and predicting gear box faults. This study explores the use of machine learning to develop an automated fault diagnosis system for gear boxes using vibration signals. The performance of the developed model is compared with existing methods to determine the most effective algorithm. This research paper explores the application of machine learning techniques for fault diagnosis of gear boxes using vibration signals. The study involved collecting vibration data from gear boxes in both good and defective conditions, under various loading conditions. Statistical features were extracted from the collected data and used to develop a fault identification system. The performance of the developed model was evaluated and compared with existing methods. The study also aimed to determine the most suitable algorithm for the collected data. Overall, the paper provides insights into the effectiveness of using machine learning approaches for gear box fault diagnosis and identifies the best-performing algorithm for this task.