Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research as a result of their complex origins and highly variable propagation. In this work, we built SEPNET , an innovative multi‐task neural network that jointly predicts future solar eruptive events, including solar flares and coronal mass ejections (CMEs) and SEPs, incorporating long short‐term memory and transformer architectures that capture contextual dependencies. SEPNET is a machine learning framework for SEP prediction that utilizes an extensive set of predictors, including the properties of solar flares, CMEs, and space‐weather HMI active region patches (SHARP) magnetic field parameters. SEPNET is rigorously evaluated on the SEPVAL SEP data set (Whitman, 2025b, https://doi.org/10.5281/zenodo.15555244 ), which is used to evaluate the performance of current SEP prediction models. The performance of SEPNET is compared with classical machine learning methods and current state‐of‐the‐art pre‐eruptive SEP prediction models. The results show that SEPNET , particularly with SHARP parameters, achieves higher detection rates and skill scores while maintaining the suitability for real‐time space weather alert operations. Although class imbalance in the data leads to relatively high false alarm rates, SEPNET consistently outperforms reference methods and provides timely SEP forecasts, highlighting the capability of deep multi‐task learning for next‐generation space weather prediction.