Extreme wildfire events can substantially perturb the global radiative balance and cause severe transboundary air pollution. Their aerosol plumes evolve rapidly on hourly timescales, yet low Earth orbit satellites have limited revisit frequency, <span>while geostationary satellites have restricted hemispheric coverage. The Earth Polychromatic Imaging Camera (EPIC) aboard</span> NOAA’s Deep Space Climate Observatory (DSCOVR), located at the Sun–Earth L1 point, provides continuous observations of the sunlit Earth disk with high temporal and unique aerosol sensitivity from ultraviolet to oxygen absorption bands. Here, we use an end-to-end deep learning framework for hourly global AOD retrieval using a parameter-efficient BatchEnsemble-based architecture, enabling efficient large-scale inference and quantitative uncertainty estimation. The framework integrates multispectral across UV, visible, and O₂ A/B bands with meteorological and topographic information. Against AERONET observations, the retrieval achieves R = 0.909, InEE = 90.91%, MAE = 0.039, and RMSE = 0.066 in sample-based validation, and maintaining robust performance under spatial and interannual extrapolation, with InEE consistently exceeding 74%. SHAP analysis reveals physically meaningful feature contributions, with UV bands contributing 58.9%, followed by visible bands (18.7%), spatiotemporal features (10.0%), meteorological parameters (6.7%), and O₂ A/B bands (4.7%). The retrieval also outperforms operational EPIC products and provides substantially more valid retrievals. Case studies over Canada, Australia, and the western United States demonstrate the retrieved hourly AOD captures the intraday evolution and long-range transport of wildfire smoke. These results highlight the potential of DSCOVR/EPIC and deep learning for continuous global monitoring of rapidly evolving aerosol events.