<p>Bearings are crucial components of induction motors (IMs) and are highly susceptible to faults or failures due to severe operating and environmental conditions. Consequently, accurate identification of bearing faults is essential for ensuring the reliable operation of IMs. The aim of this manuscript is to systematically review and evaluate diverse techniques for diagnosing bearing faults, emphasizing their effectiveness and evolution through the analysis of vibration signals. This article conducts a comprehensive and systematic review of 232 research documents focused on techniques commonly used to detect bearing defects in IMs based on vibration signals. In addition, the manuscript offers an analysis of research trends in vibration signal processing, feature extraction and reduction methods, and machine learning-based techniques for bearing fault diagnosis. The study demonstrates that advanced time–frequency methods for vibration feature extraction, when integrated with feature reduction techniques and machine learning tools, provide effective results for bearing fault diagnosis. Furthermore, emerging techniques such as deep learning algorithms, graph embedding methods, edge computing, and generative adversarial networks (GANs) enhance the accuracy of bearing defect diagnosis. This review manuscript broadly analyzed recent advancements in bearing fault diagnosis of three-phase IMs using vibration signal analysis, highlighting key trends in signal processing, feature extraction and reduction, and machine learning techniques. It provides valuable insights for developing more intelligent and reliable diagnostic approach in future industrial applications.</p>