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