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Abstract The prediction of solar energetic particle (SEP) events garners increasing interest as space missions extend beyond Earth’s protective magnetosphere. These events, which are, in most cases, products of magnetic-reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly space exploration. In this work, we utilize the recently developed data set that combines the Solar Dynamics Observatory/Space-weather Helioseismic and Magnetic Imager Active Region Patches and the Solar and Heliospheric Observatory/Space-weather Michelson Doppler Imager Active Region Patches. We employ a suite of machine learning strategies, including support vector machines (SVMs) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 ± 0.1 (experimental setting), which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive–negative imbalance) reveals a slight increase (+0.04 ± 0.05) in the accuracy of a 14 hr SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.