The vehicle market is evolving rapidly. New players are entering the market, many variants of a vehicle are investigated prior to freezing the design, and more. In this context, vehicle aerodynamics is ever more crucial. It directly impacts the vehicle range and plays a major role in meeting regulation targets. Vehicle manufacturers must also keep in mind the need for a shorter time-to-market, where one must design faster and not permit late-stage redesign. Therefore, faster assessment of vehicle aerodynamics is imperative. Computational fluid dynamics (CFD) has opened the door to virtual aerodynamic testing, allowing manufacturers to test their vehicle shapes before developing a costly and time-intensive prototype that then needs to be experimented on using a wind tunnel. While high-fidelity CFD, such as the PowerFLOW software from Dassault Systèmes, will remain an integral part of the aerodynamic development process of major OEMs, the growth of machine learning (ML) and continual improvement of its algorithms has opened doors to speed-up computational aerodynamics. The current work illustrates how aerodynamic data obtained from PowerFLOW combined with a transformer can enable car manufacturers to obtain clean 3D contour plots of the vehicle’s surface X-force distribution and the associated integrated vehicle drag force within several minutes on a single GPU (after training of the ML model). This represents a significant reduction in computational cost and time.