Predicting air pollution levels is crucial because of the damage it may do to ecosystems and people's health. A comprehensive review of current methods and models for forecasting air pollution has been presented in this article. A thorough review of 32 scholarly publications explores various machine learning algorithms, statistical models, and hybrid approaches to predict future pollution concentrations accurately. We discuss the unique challenges of air quality prediction, including data variability, spatial-temporal correlations, and the effect of meteorological factors. The research divides existing prediction models into categories based on their methodologies, data requirements, and application scenarios, stressing their strengths and limitations. The study also looks at how new technologies like IoT sensors, deep learning, and ensemble techniques might improve the precision and dependability of air pollution predictions. Future research focuses on integrating real-time data, multi-source information fusion, and developing scalable, interpretable models for dynamic air quality management
Umasanthiya et al. (Sat,) studied this question.