This systematic review by M. Pandiyan and T. Narendiranath Babu focuses on fault diagnosis in Rolling-Element Bearings (REBs) to ensure smooth machinery operations and enhance system reliability. The study aims to address the challenges posed by REB faults, which can lead to unexpected breakdowns and increased maintenance costs. The review covers a wide range of fault diagnosis methods, including signal processing techniques such as EMD, EEMD, VMD-EMD, PEEMWD, FAEMD, APSFDM, CEEMD, SVD, GBMD, IENEMD-ATD, EEMD-MEMD-BP, FPSEWT, TKEO, Adaptive spectral kurtosis technology, AMOMEDA, DAMM-SUM, EFD, SEAEFD, CREMD-ATD, FDM, and Machine Learning algorithms like KNN, ANN, ANN-DA, LSSVM, MSPC, BSE, DFAE, ANN-KNN, Deep Learning algorithms like CNN, DNN, DCNN, Deep ResNet Structure, PCNN, Multi-task CNN, TL-SPF, CNN-ResNet Structure, PNN-FIFD, VSI-CNN, CNN-GAF, and Reinforcement Learning algorithms like Deep Reinforcement Learning, Neural Network with RL, Deep feature enhanced RL, DL-RL, PPO, ML-TRL, DRTCNN, RL-NAS, and DEPDRl. The review is structured into three main perspectives: analysis of various fault diagnosis techniques, analysis of EMD-based techniques, and performance measures of existing techniques. The study concludes by highlighting the limitations and challenges in current REB fault diagnosis methods, providing valuable insights for future research and practical applications.This systematic review by M. Pandiyan and T. Narendiranath Babu focuses on fault diagnosis in Rolling-Element Bearings (REBs) to ensure smooth machinery operations and enhance system reliability. The study aims to address the challenges posed by REB faults, which can lead to unexpected breakdowns and increased maintenance costs. The review covers a wide range of fault diagnosis methods, including signal processing techniques such as EMD, EEMD, VMD-EMD, PEEMWD, FAEMD, APSFDM, CEEMD, SVD, GBMD, IENEMD-ATD, EEMD-MEMD-BP, FPSEWT, TKEO, Adaptive spectral kurtosis technology, AMOMEDA, DAMM-SUM, EFD, SEAEFD, CREMD-ATD, FDM, and Machine Learning algorithms like KNN, ANN, ANN-DA, LSSVM, MSPC, BSE, DFAE, ANN-KNN, Deep Learning algorithms like CNN, DNN, DCNN, Deep ResNet Structure, PCNN, Multi-task CNN, TL-SPF, CNN-ResNet Structure, PNN-FIFD, VSI-CNN, CNN-GAF, and Reinforcement Learning algorithms like Deep Reinforcement Learning, Neural Network with RL, Deep feature enhanced RL, DL-RL, PPO, ML-TRL, DRTCNN, RL-NAS, and DEPDRl. The review is structured into three main perspectives: analysis of various fault diagnosis techniques, analysis of EMD-based techniques, and performance measures of existing techniques. The study concludes by highlighting the limitations and challenges in current REB fault diagnosis methods, providing valuable insights for future research and practical applications.