Human motion recognition using deep learning is an active research area in computer vision and pattern recognition that focuses on understanding and classifying human activities from videos, skeleton data, or sensor signals. Early approaches employed convolutional neural networks (CNNs) for extracting spatial features from video frames, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks captured temporal dynamics of motion sequences. Recent research integrates hybrid CNN–RNN architectures, graph convolutional networks (GCNs) applied to skeleton data, and attention mechanisms to model inter-joint correlations and long-range temporal dependencies. Transformer-based models and 3D convolutional networks (3D-CNNs) have further improved accuracy in complex motion recognition tasks by simultaneously capturing spatio-temporal features. Applications include human–computer interaction, surveillance, sports analytics, healthcare monitoring, and autonomous systems, where precise recognition of human activities is critical. Current studies also explore multi-modal motion recognition, domain adaptation, and lightweight models for real-time inference on edge devices.