<p>Air pollution is one of the major environmental issues in Iran, driven by industrial greenhouse gas emissions, vehicle exhaust, power plants, and natural sources such as dust storms. Among various pollutants, PM₂.₅ is especially concerning due to its high prevalence in urban areas. Governments have utilized air quality monitoring stations to measure PM₂.₅ concentration levels to implement policies aimed at controlling this pollutant. Fixed monitoring stations, which operate for regulatory purposes, are typically located in urban areas where a large portion of the population resides. However, the measurements obtained from these stations are limited due to their insufficient number, uneven distribution, and inadequate spatial coverage, which prevent continuous pollutant estimation over large areas and the generation of comprehensive risk maps. To address these limitations, this study introduces a novel hybrid modeling framework for modeling the spatiotemporal distribution of PM₂.₅ across Iran. The proposed approach integrates Cellular Automata with the XGBoost algorithm, leveraging both satellite-based products and ground observations. Within this framework, XGBoost generates continuous transition potentials for each neighborhood window, enabling dynamic representation of spatial variations in PM₂.₅ levels. The resulting model achieved an adjusted R² of 0.88, demonstrating strong performance in capturing regional and temporal variability. A key strength of the method is its ability to localize the influence of predictor variables across different regions, allowing the development of region-specific models within each neighborhood window and improving both interpretability and predictive accuracy.</p>