Emotion identification from speech has become an important field of study in human–computer interaction, sentiment analysis, and mental health monitoring. This research compares machine learning (ML) with deep learning (DL) strategies for recognizing emotions through speech. Support vector machines (SVM), a traditional machine learning technique, is contrasted with advanced deep learning models like long short-term memory (LSTM) networks and convolutional neural networks (CNN). To accomplish Speech Emotion Recognition (SER), raw audio signals must be cleaned and normalized. Feature extraction finds essential speech parameters such as Mel-frequency cepstral coefficients (MFCCs), energy, and pitch whereas classification categorizes retrieved features using algorithms such as SVM, CNN, and LSTM based on underlying emotions. This research compares various models based on accuracy, training duration, and computational complexity. SVM with cross validation performs better when it is necessary to extract features from complicated datasets. This comparative perspective helps to identify optimal models based on application-specific needs.