Pollution is one of the major global problems nowadays. At present, air pollution is a major issue with grave effects on the environment, human health, and the economy. Advanced computational methods for modeling, simulating, and forecasting air pollution help to understand and solve issues of air quality in both urban and rural environments. In this chapter, advanced techniques in modeling air pollution, such as hybrid models, numerical simulation approaches, and machine learning algorithms, are thoroughly reviewed in this chapter. Mostly using conventional models, including the urban airshed model, CALPUFF, chemical transport models, and computational fluid dynamics, we have evaluated air quality at several geographical levels. Recent developments in computing methods, such as metamodeling chains, lower costs and maintain accuracy, thus improving the simulation process and hence the simulation of air pollution. These techniques help facilitate better decision-making in general and improve the forecasting capacity of air quality control. Moreover, deep learning, ensemble forecasting, and data assimilation techniques significantly raise the accuracy of air quality predictions. This chapter underlines how to improve real-time monitoring and prediction capabilities by using high-resolution satellite data, Internet of Things sensors, and cloud computing. Nowadays, simulating air pollution depends mostly on sophisticated, environmentally friendly, and user-friendly computer techniques. By means of strategic development, these approaches allow us to solve problems, identify unknown factors, plan ahead, and build strong models for urban air quality. Ongoing research continually improves these approaches, offering important perspectives on pollution dynamics and informing policy decisions to advance public health and environmental sustainability.