The sun routinely produces powerful bursts of energy, such as solar flares and coronal mass ejections, that can travel through space and impact Earth. These solar eruptions are major drivers of space weather, posing serious risks to critical technologies that support everyday life, including GPS systems, power grids, communication satellites, and aviation safety. While there are many space weather forecasting methods available, their long-term reliability and transparency remain major concerns for stakeholders, including scientists, policymakers, engineers, and emergency response planners. Most space weather forecasting methods operate as "black boxes," where predictions are made without clear explanations of how or why certain outcomes are reached. This lack of interpretability reduces trust in the models and limits their practical value in high-stakes decision-making situations. This project addresses this challenge by developing data-driven learning methods that are not only accurate but also explainable. By building these innovative tools to help scientists and decision-makers understand how solar activity connects to space weather events and how predictive models reach their conclusions, the project enhances the accountability, transparency, and usability of space weather forecasts. This project develops modular cyberinfrastructure for interpretable and explainable space weather prediction systems, with a focus on solar transient events. These efforts advance both hel