Emotion identification from voice and music is an essential component of human–computer interaction, which creates opportunities for entertainment, mental health monitoring, and intelligent personal assistants. This paper explores a hybrid deep learning strategy that combines convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to efficiently detect emotions expressed through audio signals. Spatial features are extracted from audio spectrogram using CNNs, whereas the temporal dynamics which are present in sequential data are extracted by LSTMs. The extraction of Mel-frequency Cepstral Coefficients (MFCCs), spectrograms of raw data is processed, emotions like happy, sad, angry, and neutral were classified. The public available datasets like CREMA-D and RAVDESS are used for real-world emotion recognition, the robustness and scalability are demonstrated.