Facial expression recognition (FER) using deep learning is an active research area in computer vision and affective computing that focuses on automatically identifying human emotions from facial images or videos. Early deep learning approaches utilized convolutional neural networks (CNNs) to extract hierarchical spatial features from facial regions, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks were employed to capture temporal dynamics in video sequences. Subsequent research introduced attention mechanisms, 3D-CNNs, and hybrid CNN–LSTM architectures to enhance sensitivity to subtle expression changes and improve temporal modeling. More recent advances leverage graph convolutional networks (GCNs) to model facial landmarks, transformer-based architectures for global context modeling, and multimodal fusion integrating audio, physiological signals, or depth data to boost recognition accuracy. Applications of FER span human–computer interaction, mental health monitoring, driver fatigue detection, surveillance, and social robotics. Current research also focuses on cross-dataset generalization, robustness to occlusions, pose variations, illumination changes, and real-time deployment for practical systems.