The twenty-first century faces air pollution as a major environmental problem that creates severe risk to human health and the worldwide ecosystem. Fine particles of pollutants, nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) cause respiratory diseases and cardiovascular problems and cause premature deaths. Worldwide increasing levels of urbanization and industrialization have caused air quality problems, hence need for effective air quality monitoring. Traditional methods of air quality monitoring, such as ground sensors and satellite observations, provide valuable information but are often limited by spatial resolution, cost, and time. Forecast models, which rely heavily on numerical weather prediction (NWP) techniques, while useful, also face limitations in predicting highly localized and dynamic changes in air quality. As air pollution becomes increasingly complex to monitor by road diversity of sources, emissions, and climatic conditions, more advanced computational approaches are required to distribute accurate and real-time air quality information. This chapter examines how advanced algorithms, particularly ML and DL techniques, transform air quality monitoring and forecasting operations. The combining of AI and machine learning technology enables better prediction accuracy and faster responses to air quality problems, which benefits urban health. The combination of artificial intelligence with Internet of Things technologies enables continuous data collection from sensors installed throughout urban areas. The Sensors measure multiple environmental factors, including particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and weather conditions. The implementation of deep learning (DL), machine learning (ML), and data assimilation methods enhances the accuracy and reliability of air quality forecasts. Advanced algorithms that include short-term memory networks (STM) and convolutional neural networks (CNNs) allow these models to process real-time temporal and spatial data, leading to significant improvements for forecasting and air quality monitoring. The algorithms improve both the accuracy of air quality index predictions and provide early warnings for pollution prevention.