Deep learning has emerged as a powerful approach for traffic congestion prediction, leveraging its ability to model complex spatial–temporal dependencies in transportation networks. Research in this area explores a variety of architectures, including convolutional neural networks (CNNs) for capturing spatial correlations among road segments, recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for temporal sequence learning, and hybrid CNN–LSTM models that integrate both spatial and temporal features for more accurate forecasting. Recent works also employ graph neural networks (GNNs) and attention mechanisms to model dynamic traffic flow across irregular road networks, while generative adversarial networks (GANs) and transfer learning approaches are being explored for handling sparse data and cross-city prediction. These deep learning methods have been successfully applied to predict short-term traffic congestion levels, estimate travel times, and optimize intelligent transportation systems, demonstrating significant improvements over traditional statistical and shallow learning models in terms of prediction accuracy, robustness, and real-time adaptability.