Air pollution poses a significant global threat to environmental sustainability and human health, leading to millions of premature deaths annually. Traditional forecasting methods often do not adequately address the complex, nonlinear interactions between environmental variables. This chapter explores the application of artificial intelligence (AI) for forecasting air pollution and related health outcomes. The emphasis is placed on hybrid models that combine multiple approaches to improve prediction accuracy. Key insights include the application of AI to predict major pollutants such as PM2.5, PM10, O3, SO2, and CO2, as well as its role in early warning systems and health impact assessments. The findings underscore the potential of AI-driven models to improve air quality management and inform policy decisions. Challenges related to data scarcity, computational resources, and model scalability are also discussed, along with future directions for research and implementation.