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A coupled framework of ballistic simulations and an optimized machine learning (ML) model was developed to accurately predict the kinetic energy reduction of a projectile impacting a target. ML models can require a significant number of data points for proper training, testing, and validation. High-performance computing (HPC) resources can be used to simulate the ballistic impacts of various projectiles against several different target materials using appropriate physics-based hydrocodes. Computational modeling can explore areas where experiments would naturally be cost-prohibitive. These hydrocodes can evaluate large parametric spaces varying the projectile and target variables that are required to train an ML model. In this study a large, generated set of data points was used to develop an optimized artificial neural network (ANN) algorithm to create a fast-running model without prior knowledge of the mathematical relationships between all the input and output variables. The optimized ANN model was developed using Optuna in an HPC environment to tune the hyperparameters needed for the ANN model. This fast-running ML model could then be leveraged to investigate designing optimized targets that could protect against different types of projectiles. The results of this work showed that the optimized ANN model predicted the kinetic energy reduction with a mean absolute percentage error of 2.7% across the validation data. Overall, the optimized ANN model showed excellent agreement across the range of data considered by the computational models. [ABSTRACT FROM AUTHOR]