Air pollutants such as nitrogen dioxide (NO<sub>2</sub>) have detrimental effects on human health and ecosystems. It is therefore very crucial to pinpoint the location of high pollutant concentrations over large areas. Ground-based stations, while offering continuous temporal measurements, cannot provide broader spatial coverage for regions like cities. This study uses Tropospheric Emissions: Monitoring Pollution (TEMPO) satellite observations and a machine learning model to estimate high-resolution surface-level NO<sub>2</sub> concentrations over the Greater Toronto Area (GTA), Ontario, Canada. The random forest regression model was trained with input parameters such as hourly tropospheric NO<sub>2</sub> vertical column density (VCD) values and boundary layer height (BLH), which are the two most effective parameters in feature importance. The model achieved a coefficient of determination (R<sup>2</sup>) of 0.84, a root mean square error (RMSE) of 1.703 ppb, and a mean absolute error (MAE) of 0.939 ppb, indicating strong and reliable predictive performance. The findings of this research can support air quality forecasting, public health studies, and urban planning decisions, especially in regions with scarce ground-based pollutant data.