The main global problem is air pollution, as it seriously affects ecosystems, human health, and temperature. Computational approaches to assess air pollution have helped simplify the quantification, prediction, and mitigation of these risks. Modern algorithms, machine learning (ML), and numerical simulations allow these methods to highlight the complex dynamics of air contaminants, including their sources, routes of transport, and effects. Although effective, conventional monitoring techniques often show limits in both spatial reach and breadth. On the contrary, utilizing satellite data, ground sensors, and emission inventories, computer models offer thorough evaluations in large geographic areas. Principal computer approaches recreate the movement and dispersion of pollutants including PM2.5, NOx, and ozone through the use of numerical models, including Gaussian dispersion models and the Community Multiscale Air Quality (CMAQ) model. These models holistically grasp pollution dynamics by including meteorological factors, geography, and atmospheric chemistry. Recent developments in machine learning have helped make real-time air quality forecasts, using prior data, more precise. Computational methods simplify scenario analysis, allowing academics and decision-makers to evaluate the consequences of several mitigating strategies, such as urban design modification or emission reduction. Integration of these models with geographic information systems (GIS) enables geographical visualization that helps to detect pollution hotspots and historical patterns. Though effective, computational models have many shortcomings, such as the need for perfect input data and processing capability. But with the growing availability of big data and the increase in processing capabilities, which allow better informed decisions to be made in the battle against air pollution, these techniques are more indispensable for controlling air quality.