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While variations in Shiga toxin subtype are well-recognized drivers of disease severity, many unexplained differences remain among strains carrying the same toxin profile.\n                  <\/jats:p>\n                  <jats:p>We applied explainable machine learning (ML) approaches \u2013 Random Forest and Extreme Gradient Boosting \u2013 to whole-genome sequencing data from 1,030 STEC O157:H7 isolates to predict patient clinical outcomes, using data collected over 2 years of routine surveillance in England. A phylogeny-informed cross-validation strategy was implemented to account for population structure and avoid data leakage, ensuring robust model generalizability. Extreme Gradient Boosting outperformed Random Forest in predicting minority classes and correctly predicted high-risk isolates in traditionally low-risk lineages, illustrating its utility for capturing complex genomic signatures beyond known virulence genes. Feature importance analyses highlighted phage-encoded elements, including potentially novel intergenic regulators, alongside established virulence factors. Moreover, key genomic regions linked to small RNAs and stress-response pathways were enriched in isolates causing severe disease. 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