This project proposed and evaluated a conditional generative adversarial network surrogate model that accounts for the contribution of urban morphologies to pedestrian wind flow conditions. The validity of the approach was demonstrated at a fraction of the time that would be required to perform the equivalent conventional simulation. Variations in dataset encoding techniques, image resolutions and geometric diversity of the training set were explored to identify the key parameters affecting model’s accuracy and suitability. The model was deployed within a CAD environment for interactive near real-time performance feedback during iterative design.