Air pollution is a significant threat to the environment and public health worldwide. In the context of rising urbanization and industrialization, accurate air quality forecasts and control have become increasingly important. Traditional statistical and deterministic models typically fail to account for the complex, nonlinear, and dynamic behavior of air contaminants. In this context, Artificial Intelligence (AI) and Machine Learning (ML) approaches have developed as useful tools for evaluating large and diverse environmental information, leading to more precise forecasts and effective pollution mitigation approaches. This comprehensive analysis focuses on the present state of AI and machine learning applications in air quality prediction and pollution mitigation. It discusses various models for temporal and geographic forecasting, source distribution, and real-time monitoring, such as regression algorithms, decision trees, neural networks, support vector machines, and deep learning approaches. The assessment also emphasizes the use of AI, IoT devices, remote sensing data, and geospatial analytics to enhance pollution control systems. Additionally, it covers issues related to data integrity, model interpretability, and scalability, while also highlighting key areas for future research and practical implementations. This review aims to serve as a valuable resource for environmental academicians, policymakers, and technologists working on resilient air quality monitoring.
Nishat et al. (Wed,) studied this question.