The prediction of Electronic Stopping Power for general ions and targets is aproblem that lacks a closed-form solution. While full approximate solutionsfrom first principles exist for certain cases, the most general model in use isa pseudo-empirical model. This paper presents our advances towards creatingpredictive models that leverage state-of-the-art Machine Learning techniques. Akey component of our approach is the training data selection. We show results thatoutperform or are on par with the current best pseudo-empirical Stopping Powermodel as quantified by the Mean Absolute Percentage Error metric.