Speech emotion recognition (SER) plays a key role in human–computer interaction by enabling systems to understand and respond to emotions. Traditional machine learning methods such as support vector machines (SVMs) and random forest (RF) rely on handcrafted features, limiting their accuracy. Deep learning models, including convolutional neural networks (CNNs) and long short-term memory (LSTMs), have improved SER performance by capturing spatial and temporal patterns. The CNN-LSTM hybrid model provides a balance between accuracy and speed, achieving 88.2% accuracy. However, transformer-based models like Wav2Vec2.0 demonstrate superior adaptability with state-of-the-art performance, achieving 91.6% accuracy through self-supervised learning. Feature extraction techniques, such as mel-spectrograms, improve classification performance over Mel-Frequency Cepstral Coefficients (MFCCs) by 3–5%. Data augmentation enhances model robustness, while optimization techniques like quantization and pruning ensure real-time feasibility. Future research should focus on multimodal SER, cross-language adaptation, speaker variability challenges, and real-time edge AI deployment to develop more scalable SER systems for applications in virtual assistants, healthcare, and customer service automation.