This meticulously designed framework has revolutionized machine monitoring automation in various industries by significantly reducing the need for manual oversight and interpretation. A data-driven framework for monitoring gearbox condition has been developed based on the analysis of simulated vibration signals. Signal analysis, feature extraction, and machine learning algorithms are used to effectively assess the machinery’s condition. In order to avoid extensive manual involvement, the proposed method focuses on data-driven strategies and relies less on signal processing. The proposed method relies less on signal processing than on extensive manual involvement to avoid extensive manual involvement. The system’s sensitivity to data variations is enhanced by using statistical process control techniques, which enables early detection of faults and helps to prevent potential losses.