Computer Fluid Dynamics (CFD) airflow simulations are not used as often in architectural settings primarily due to time constraints. The proper use of CFD airflow simulations involves a complex setup and run-time process that requires a large amount of expertise on the different stages. This study aims to apply existing generative machine learning algorithms to compute CFD wind velocity simulations to significantly shorter run times while maintaining a relatively high accuracy level. In order to test the proposed hypothesis that machine learning can be used as a method to produce rapid and acceptable results for airflow CFD simulations in the early design stages, multiple machine learning models were created, trained, and tested. The evaluation metrics consisted of using different image similarity methods to evaluate the accuracy of the images produced by the machine learning model compared to their CFD engine counterparts. In addition, run times between the CFD engine and the trained machine learning model were recorded and compared. These results obtained indicate that GAN application for CFD airflow predictions can produce acceptable results showing a significant run time difference of over a minute between the CFD simulation and the machine learning model.