State-of-the-Art and Emerging Trends in Deep Learning for Air Quality Forecasting and Health Impact Modeling: A Comprehensive Review

Air pollution is now a concern globally not just to the environment but also to human health. In the past decade, deep learning approaches have become more popular compared to traditional machine learning in air quality forecasting because they promise to emulate complex spatiotemporal patterns of airborne pollution. The predictive accuracy has considerably increased; however, integration with impact evaluation for health implications is poor. This review synthesizes existing DL method- ologies for AQI forecasting and their unifications within health hazard models. It provides a critical survey of more than 150 relevant, peer-reviewed papers on various architectures such as CNNs, RNNs, LSTMs, GRUs, GNNs, and Transformers. It addresses hybrid models, interpretable models such as SHAP and attention, and health-integrated models that connect pollutant exposure with outputs such as child mortality data and respira- tory disease. The document discusses important challenges such as heterogeneity of data, interpretability of the models, domain adaptation, and temporal alignment of health datasets. Besides, it provides benchmark datasets, evaluation metrics, and gaps in the field. Trends such as multitask learning, spatiotemporal fusion networks, and explainable AI are discussed in an attempt to guide upcoming research at the intersection of environmental informatics and health analytics.