Urban areas face a significant health risk due to atmospheric pollution necessitating continuous monitoring to assess and mitigate its impact and achieve a sustainable city. In this study, a deep learning model was developed and trained using Computational Fluid Dynamics (CFD) simulations to predict the dispersion of nitrogen dioxide () emissions from traffic. The model’s performance was evaluated by comparing it to real-world field measurements conducted in Antwerp, Belgium, throughout 2016. Temporal comparisons with a traffic-influenced air quality station over the entire year yielded an average correlation coefficient (R) of 0.83 and a mean relative error (MRE) of 0.21. Additionally, a spatial evaluation was conducted by comparing the model’s predictions to a measurement campaign involving 73 samplers. The spatial evaluation resulted in an R of 0.72 and a MRE of 0.18 for samplers located near known emission sources. Notably, the deep learning model demonstrated computational efficiency, outperforming CFD simulations by an order of magnitude of 100 to 1000, enabling real-time pollution monitoring and large-scale scenario studies without compromising the consideration of micro-scale effects that are typically overlooked in larger-scale models.