This systematic review summarizes the current state of fault diagnosis methods for rolling-element bearings (REBs). The purpose is to ensure the safe and smooth operation of machinery by detecting bearing faults early, which can prevent unexpected breakdowns and reduce maintenance and downtime costs. The paper reviews various signal processing techniques, including EMD, EEMD, VMD-EMD, PEEWMD, FAEMD, APSFDM, CEEMD, SVD, GBMD, IENEMD-ATD, EEMD-MPE-BP, FPSEWT, TKEO, adaptive spectral kurtosis technology, AMOMEDA, DAMM-SUM, APSFDM, EFD, SEAEFD, CEEMD-ATD, FDM, among others. It also examines machine learning algorithms such as KNN, ANN, ANN-DA, LSSVM, MSPC, BSE, DFAE, ANN-KNN, and 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 such as Deep Reinforcement Learning, Neural Network with RL, Deep feature enhanced RL, DL-RL, PPO, ML-TRL, DRTCNN, RL-NAS, and DEPDRL. The results show that existing fault diagnosis methods are analyzed from three perspectives: (1) analysis of various REB fault diagnosis techniques, (2) analysis of EMD-based REB fault diagnosis techniques, and (3) analysis of performance measures of existing REB fault diagnosis techniques. The conclusion highlights the limitations and challenges in current REB fault diagnosis techniques. Researchers aiming to develop efficient methods for early and effective diagnosis can find useful information and future directions in this survey. Keywords: Rolling-element bearing, REB faults, EMD technique, Machine learning, Deep learning, Reinforcement learning.This systematic review summarizes the current state of fault diagnosis methods for rolling-element bearings (REBs). The purpose is to ensure the safe and smooth operation of machinery by detecting bearing faults early, which can prevent unexpected breakdowns and reduce maintenance and downtime costs. The paper reviews various signal processing techniques, including EMD, EEMD, VMD-EMD, PEEWMD, FAEMD, APSFDM, CEEMD, SVD, GBMD, IENEMD-ATD, EEMD-MPE-BP, FPSEWT, TKEO, adaptive spectral kurtosis technology, AMOMEDA, DAMM-SUM, APSFDM, EFD, SEAEFD, CEEMD-ATD, FDM, among others. It also examines machine learning algorithms such as KNN, ANN, ANN-DA, LSSVM, MSPC, BSE, DFAE, ANN-KNN, and 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 such as Deep Reinforcement Learning, Neural Network with RL, Deep feature enhanced RL, DL-RL, PPO, ML-TRL, DRTCNN, RL-NAS, and DEPDRL. The results show that existing fault diagnosis methods are analyzed from three perspectives: (1) analysis of various REB fault diagnosis techniques, (2) analysis of EMD-based REB fault diagnosis techniques, and (3) analysis of performance measures of existing REB fault diagnosis techniques. The conclusion highlights the limitations and challenges in current REB fault diagnosis techniques. Researchers aiming to develop efficient methods for early and effective diagnosis can find useful information and future directions in this survey. Keywords: Rolling-element bearing, REB faults, EMD technique, Machine learning, Deep learning, Reinforcement learning.