Modeling and simulation (M&S) tools are used extensively throughout the Ground Vehicle Systems Center and the U.S. Army to perform an analysis of ground vehicles more quickly and less expensively than through physical testing. The Computational Research and Engineering Acquisition Tools and Environments-Ground Vehicles (CREATE-GV) project is an M&S software effort that focuses on mobility and autonomous vehicle simulation and analysis, using physics-based three-dimensional modeling to accurately calculate a variety of ground vehicle metrics and parameters of interest. However, because these simulations are high fidelity, they often require a great deal of computational power and time. One approach to reducing simulation time that has proved effective in certain contexts is the creation of “surrogate models” through machine learning (ML) algorithms. However, it is often very challenging to accurately predict the mobility of a ground vehicle system in general, and there is no existing model that can predict the mobility of autonomous systems. A great deal of uncertainty exists in the mobility and autonomy area of physics-based simulation models related to modeling assumptions, terrain conditions, and insufficient knowledge of interactions between the vehicle and terrain. Understanding how the uncertainties inherent in autonomous mobility prediction affect model accuracy is still an open fundamental research question. We present a surrogate modeling approach leveraging ML algorithms to work with CREATE-GV to increase the computation speed of mobility assessments while still considering the reliability of the mobility predictions under uncertainty.