The application of machine learning techniques in fault diagnosis represents a significant advance in efficiency and accuracy in the early detection of problems and proposes applicable solutions in industrial machinery and engineering systems in general. [PERSON] al. used an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings [19]. In fact, machine learning techniques have become an essential part of asset management and predictive maintenance in various sectors, such as industrial, energy, aeronautical and automotive. In this vein, machine learning optimizes error detection in complex systems, as prediction algorithms can learn to recognize operational data patterns in real time, allowing them to identify subtle anomalies that human operators may not notice. This is achieved by training machine learning algorithms on historical data containing examples of normal conditions and known errors. Once trained, these algorithms can then generate alerts when they detect deviations from the normal model. Furthermore, machine learning can efficiently process large amounts of data, which is especially useful for troubleshooting. It should be noted that modern machines generate large volumes of data from the implantation of sensors that measure vibration, temperature, pressure and other physical parameters, and machine learning algorithms are able to process and analyze the information quickly and accurately. This makes it easier to detect problems early and, consequently, reduces the manual workload. In recent years, deep learning techniques have significantly improved fault identification systems across various industries. Among these techniques, Long Short-Term Memory networks (LSTMs), autoencoders (AEs), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Generative Adversarial Networks (GANs) have been widely used. LSTM networks, a specialized type of RNN, are particularly effective in capturing long-term dependencies in sequential data, making them suitable for time-series analysis in fault detection. Studies, such as the one in [20], have demonstrated that LSTM-based approaches can accurately model temporal patterns and detect anomalies indicative of faults. Similarly, autoencoders, which learn compressed representations by reconstructing input data, have been employed for anomaly detection by training on normal operational data, with high reconstruction errors signaling faults. Research, including [21], highlights the effectiveness of autoencoder-based methods, especially when combined with LSTM networks for improved performance. CNNs, known for their ability to extract spatial features, have been applied in fault detection by analyzing patterns in sensor data or images. Their hierarchical feature extraction capabilities make them well suited for identifying complex fault signatures, as seen in [22], which discusses the integration of CNNs with other DL models for enhanced fault diagnosis. RNNs, designed to process sequential data by maintaining a state that captures information from previous inputs, have also been utilized in fault detection. However, traditional RNNs often struggle with long-term dependencies, a limitation that LSTMs address. Lastly, GANs, which consist of a generator and a discriminator network trained adversarially, have been increasingly explored in fault detection. GANs can generate synthetic fault data to augment training datasets or detect anomalies by evaluating the discriminator's ability to differentiate between normal and faulty data. The integration of these DL techniques has significantly enhanced fault identification, with each method contributing unique advantages--LSTMs and RNNs excel in modeling temporal dependencies, AEs in learning data representations for anomaly detection, CNNs in extracting spatial features and GANs in generating synthetic data to improve detection frameworks. This study uses deep learning techniques to optimize fault diagnosis in rolling element bearings of rotating electrical machines.