Solar Energetic Particles (SEPs) are at the forefront of heliophysics research, driven by their profound implications for space weather forecasting and the safeguarding of Earth’s technological infrastructure. Originating from the Sun’s dynamic events like solar flares (SFs) and coronal mass ejections (CMEs), these particles travel at near-relativistic speeds across the heliosphere, challenging our prediction and mitigation efforts. The journey from statistical methods such as Kaplan-Meier Estimation (KM) and Cox Proportional Hazards modeling (Cox PH), alongside machine learning (ML) techniques including Survival Trees (STs) and Random Survival Forests (RSFs), reflects the interdisciplinary strategy required to navigate the complexities of space weather phenomena. By reviewing a spectrum of predictive techniques, from magnetohydrodynamic models to statistical ensembles, we underscore the significance of survival analysis in determining the timing and likelihood of SEP events. The shift towards computational solutions is further justified by the rise of big data in astronomy, emphasizing the transformative impact of cloud computing, particularly through platforms like Helio-Lite, and artificial intelligence (AI) in advancing our predictive accuracy and forecasting strategies against SEPs. In this dissertation, we embark on a journey alongside SEPs as they traverse the vastness of the interplanetary medium—from their explosive origins on the Sun, across one astronomical unit in as little as eight minutes, to their detection by the Geostationary Operational Environmental Satellites (GOES) orbiting Earth—employing survival analysis to chart the timing of their voyage. This research hypothesizes that the application of survival analysis and RSF, coupled with cloud computing can enhance the accuracy and reliability of forecasting the timing and arrival of SEPs, offering a novel approach in the field of heliophysics and space weather forecasting.