Action recognition using deep learning is a key research area in computer vision that focuses on identifying and classifying human actions or activities from videos or sequences of images. Early approaches utilized convolutional neural networks (CNNs) for spatial feature extraction and recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to model temporal dynamics. Subsequent research introduced 3D-CNNs and two-stream networks to simultaneously capture spatio-temporal features, while attention mechanisms and graph convolutional networks (GCNs) on skeleton data have enhanced the modeling of inter-joint relationships and motion patterns. Recent advances leverage transformer-based architectures, temporal relational reasoning, and multimodal data fusion (e.g., combining RGB, depth, and optical flow) to improve accuracy and robustness in complex scenarios. Applications include human–computer interaction, surveillance, sports analytics, autonomous systems, and healthcare monitoring. Current research also focuses on real-time inference, domain adaptation, and learning from limited or weakly labeled data to enable practical deployment in diverse environments.