In the field of Speech Emotion Recognition (SER) the research is gaining more attention, with the ability to augment human-computer interaction by allowing systems to recognize emotional states from speech. The research area has evolved significantly, with most of the advancements being due to deep models of learning which are particularly good at extracting subtle patterns from raw audio data directly. The review discussed the current research, with focus on the use of prevalent deep learning architectures—e.g., Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN),, and hybrid models like CNN-BiLSTM—for SER tasks. The area of research has come a long way, some of the major challenges still persist. The design of dependable SER systems is still beset by issues such as imbalanced datasets, the ongoing difficulty of effective feature selection, noise sensitivity, and computational efficiency problems. Although deep learning has advanced remarkably, the review emphasizes that significant obstacles must still be overcome before the area can provide consistently trustworthy performance in actual situations. This paper proposes several future research including broadening and diversifying datasets, increasing the cross-linguistic strength of the model, and better real-time system performance.