Accurate prediction of the individualized survival benefit of adjuvant therapy is key to making informed therapeutic decisions for patients with early invasive breast cancer. Here, we use a state-of the-art automated and interpretable machine learning algorithm to develop a breast cancer prognostication and treatment benefit prediction model — Adjutorium — using data from large-scale cohorts of nearly 1 million women captured in the national cancer registries of the United Kingdom and the United States. We trained and internally validated the Adjutorium model on 395,862 patients from the UK National Cancer Registration and Analysis Service (NCRAS); we then externally validated the model among 571,635 patients from the US Surveillance, Epidemiology, and End Results (SEER) Program. Adjutorium exhibited significantly improved accuracy compared to the major prognostic tool in current clinical use (PREDICT v2.1) in both internal and external validation (AUC-ROC for 5-year survival prediction in NCRAS was 0.835, 95% CI: 0.833–0.837 and 0.755, 95% CI: 0.753–0.757 for Adjutorium and PREDICT v2.1. In SEER, the AUC-ROC performance was 0.815, 95% CI: 0.813–0.817 and 0.775, 95% CI: 0.772–0.778 for Adjutorium and PREDICT v2.1, respectively). Importantly, our model substantially improved accuracy in specific subgroups known to be under-served by existing models. Adjutorium is currently implemented as a web-based decision support tool (vanderschaar-lab.com/adjutorium/) to aid decisions on adjuvant therapy in women with early breast cancer, and can be publicly accessed by patients and clinicians worldwide.