Integrating load-bearing and energy storage capabilities within a single material system, known as multifunctional structural batteries, holds immense promise for advancing structural energy storage technologies. These systems offer significant weight reduction and enhanced safety, but their commercialization is hindered by challenges due to vast unexplored design spaces and costly trial-and-error processes. In this work, we employ an experimentally validated computational framework to accelerate the design of carbon fiber (CF)-based structural batteries impregnated with solid polymer electrolyte (SPE). To analyze the mechanical behavior, a finite element analysis (FEA) model powered by computational micromechanics was used to investigate the CF/SPE interface and damage mechanisms to predict the macro-effective material properties. To preform accurate forecasts on energy storage, a data-driven machine learning approach based on artificial neural networks (ANN) was optimized via a Bayesian optimization algorithm to predict the structural battery's future capacity. Furthermore, we validate the optimized ANN model in a rapid capacity degradation condition, showcasing the suitability of such algorithms for studying coupled multifunctional structures under mechanical and electrochemical loads, providing promising insights for optimizing the development of multifunctional composites.