Traditional forecasting methods of air pollutants show intrinsic limitations due to the complexity of atmospheric interactions. Recent research has moved toward the employment of artificial intelligence (AI)-based approaches and satellite data processing. The framework proposed in this study is a transfer learning (TL) model to estimate surface-level NO₂ concentrations across multiple locations by using satellite and environmental data. The approach integrates Sentinel-5P TROPOMI-derived tropospheric NO₂ columns, meteorological variables (temperature, precipitation, wind speed and direction), spatial coordinates and temporal features. A CatBoost regression model is implemented, leveraging a Leave-One-City-Out (LOCO) TL framework across five cities (Berlin, London, Madrid, Paris and Toronto) in the world. This enables the model transfer from multiple source domains to a new target city with minimal ground-based data. Experimental results are outperforming city-specific baseline models, by showing a reduced Root Mean Square Error (RMSE) by approximately 7% and a Coefficient of Determination (R²) higher by 2.7%. Toronto, which represents an environment with a low monitoring density, benefits most from TL, with R² improving from 0.58 (baseline) to 0.66 (transfer) and RMSE dropping from 6.44 μg/m³ to 5.84 μg/m³. A detailed Leave-One-Block-Out (LOBO) ablation study shows how each group of features contributes to the performance of the model. Spatial coordinates and meteorological features are the most influential predictors of NO₂ concentration, while the satellite NO₂ data increase model generalization. These results highlight the potential of cross-city TL and remote sensing synergy for scalable urban air pollution monitoring, especially in limited ground-based monitoring scenarios.