This study aims to assess the spatiotemporal variation of key ambient air pollutants (PM10, SO2, and NO2) and their relationship with Land Surface Temperature (LST) in Kota city, Rajasthan, during the period 2018-2023. The research integrates geospatial analysis using Google Earth Engine (GEE) for LST retrieval and Inverse Distance Weighting (IDW) interpolation for pollutant mapping. Statistical methods, including pearson correlation, simple linear regression, and multiple regression, were applied to evaluate both individual and combined effects of pollutants on LST across six monitoring stations. The results highlight a substantial decline in PM10 levels during 2020, particularly at MS-3 (Rajasthan Technical University), where concentrations fell from 182.41 mu g/m(3) in 2018 to 84.23 mu g/m(3), largely due to National Clean Air Programme interventions and COVID-19 lockdowns. However, PM10 rebounded in the following years, peaking at 139.09 mu g/m3 at MS-4 in 2023. NO2 levels reached a maximum of 35.61 mu g/m3 at MS-6 (Shrinathpuram Stadium) in 2023, while SO2 showed relatively stable but spatially variable patterns. Pearson correlation revealed a strong positive association between LST and PM10 at MS-3 (r = 0.75), with a shift from positive to negative correlations between 2018 and 2020, reflecting emission changes. Simple linear regression showed the strongest LST relationship with PM10 (R2 = 0.37), and multiple regression analysis confirmed PM10 as the only statistically significant predictor of LST (p = 0.0019), explaining 30.2% of its variation. SO2 and NO2 showed weaker and inconsistent associations with LST, indicating influence from localized emissions and meteorological factors. The findings emphasize the role of particulate matter in urban thermal dynamics and underscore the need for targeted pollution control, heat-resilient infrastructure, and continuous geospatial monitoring in rapidly urbanizing semi-arid environments.