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Solar Energetic Particle (SEP) events and their major subclass, Solar ProtonEvents (SPEs), can have unfavorable consequences on numerous aspects of lifeand technology, making them one of the most harmful effects of solar activity.Garnering knowledge preceding such events by studying operational data flows isessential for their forecasting. Considering only Solar Cycle (SC) 24 in ourprevious study, Sadykov et al. 2021, we found that it may be sufficient toutilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, wereport a catalog recording $\geq$ 10 MeV $\geq$ 10 particle flux unit SPEs withtheir properties, spanning SCs 22-24, using NOAA's Geostationary OperationalEnvironmental Satellite flux data. We report an additional catalog of dailyproton and SXR flux statistics for this period, employing it to test theapplication of machine learning (ML) on the prediction of SPEs using a SupportVector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). We explore theeffects of training models with data from one and two SCs, evaluating howtransferable a model can be across different time periods. XGBoost proved to bemore accurate than SVMs for almost every test considered, while outperformingoperational SWPC NOAA predictions and a persistence forecast. Interestingly,training done with SC 24 produces weaker TSS and HSS2, even when paired with SC22 or SC 23, indicating transferability issues. This work contributes towardsvalidating forecasts using long-spanning data -- an understudied area in SEPresearch that should be considered to verify the cross-cycle robustness ofML-driven forecasts.

Predicting Solar Proton Events of Solar Cycles 22-24 using GOES Proton & soft X-Ray flux features