The acceleration of urbanization has increasingly exacerbated air pollution in Northwest China. However, existing studies have relatively few analyses of PM2.5 concentrations in response to land-use changes. This study quantitatively evaluated the impact of land-use changes on PM2.5 concentrations in Urumqi (2014 to 2023) using remote sensing techniques and machine learning methods. The MCD19-A2 aerosol optical depth (AOD) product, with gaps filled using a singular spectrum analysis algorithm (99.63% AOD coverage), was used to predict PM2.5 concentrations based on the light gradient boosting machine method (10-CV R2=0.93, root mean square error=17.98 μg/m3). The spatial correlation between land-use changes and PM2.5 concentrations showed that PM2.5 concentrations were highest in central urban areas but decreased by an average of 27.41 μg/m3 over the decade. Land-use type transitions (barren-grassland, grassland-barren, and grassland-cropland) were significantly negatively correlated with PM2.5, indicating these changes reduced aerosol concentrations during the research period in Urumqi. The reaction of dynamic PM2.5 to land-use and land-cover changes showed a local overlap but was not entirely consistent, as reflected by the geographically weighted regression model. Geodetector quantified the contribution of land-use change to PM2.5 reduction, particularly barren-grassland conversion, which notably reduced PM2.5 (contribution coefficient = 0.161), highlighting the importance of protecting vegetated areas for PM2.5 control in Urumqi. These findings clarify the impact of land-use change on PM2.5, supporting improvements in land management and atmospheric control strategies for sustainable development in Urumqi.