<p>Air pollution has become one of the most significant environmental and public health challenges worldwide, necessitating accurate prediction systems to support effective air quality management and informed decision-making. Deep learning has emerged as a powerful approach for modelling the complex nonlinear and spatiotemporal relationships that characterise air quality data. This review provides a comprehensive synthesis of recent advances in deep learning techniques for air pollution prediction. Following the PRISMA 2020 guidelines, the literature was systematically reviewed to evaluate deep learning architectures, environmental data sources, model performance, and emerging research trends. The review traces the evolution of deep learning models from Artificial Neural Networks (ANNs) to Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Transformer-based models, transfer learning, and hybrid architectures. It further examines the integration of ground monitoring stations, satellite remote sensing, meteorological observations, IoT-based sensors, and multi-source data fusion for improving prediction accuracy and model robustness. Comparative analysis indicates that hybrid and Transformer-based models consistently achieve superior predictive performance, while RMSE, MAE, and R² remain the most widely adopted evaluation metrics. The review also identifies key research challenges, including data scarcity, model interpretability, computational complexity, and benchmark standardisation, and highlights emerging directions such as Explainable Artificial Intelligence, Graph Neural Networks, Physics-Informed Neural Networks, Federated Learning, and Edge AI for developing accurate, scalable, and intelligent air quality prediction systems.</p>