Artificial Intelligence for Air Quality Prediction: A Review of Machine Learning and Deep Learning Applications in Atmospheric Pollution Forecasting

Atmospheric pollution presents a critical global challenge to public health and ecological stability, necessitating highly accurate predictive modeling to support mitigation strategies and policy decisions. This research project provides a comprehensive evaluation and comparative analysis of artificial intelligence methodologies, specifically machine learning and deep learning architectures, applied to atmospheric pollution forecasting. The study systematically examines traditional machine learning algorithms, including Support Vector Machines, Random Forest, and Extreme Gradient Boosting, alongside advanced deep learning paradigms such as Convolutional Neural Networks, Long Short-Term Memory networks, hybrid CNN-LSTM configurations, Transformer-based models, and Graph Neural Networks. By evaluating these models across diverse spatial and temporal forecasting scenarios, the project highlights the trade-offs between predictive

accuracy, computational complexity, and model interpretability. While deep learning and attention-based architectures demonstrate superior performance in capturing complex, non-linear spatio-temporal dynamics, traditional machine learning models offer computational efficiency and viability for resource-constrained environments. The resulting framework serves as a structured guide for selecting and implementing optimal predictive models based on specific environmental datasets, computational budgets, and forecasting horizons, thereby supporting the development of robust air quality management systems.

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