A machine learning analysis of 2,734 heart failure patients in Kansas found that an XGBoost algorithm could predict 30-day hospital readmissions with 75% accuracy (AUROC=0.75). The highest-risk patients, representing just 10% of cases, accounted for one-third of all readmissions and showed 76% positive predictive value—a 3.3-fold risk enrichment over baseline rates. Key predictors included prior hospital utilization patterns, diabetes and kidney disease management quality, and overall comorbidity burden. This represents a meaningful advance in cardiovascular risk stratification, as current prediction tools often miss clinically relevant heart failure hospitalizations that aren't coded as primary diagnoses. The ability to identify the highest-risk 10% of patients could enable hospitals to concentrate intensive transitional care resources where they're most needed, potentially reducing the substantial healthcare burden of heart failure readmissions. However, this preprint study awaits peer review, and the findings need validation across diverse healthcare systems beyond Kansas. The 27% baseline readmission rate reflects the persistent challenge in heart failure management, making such predictive tools increasingly valuable for healthcare systems seeking to improve patient outcomes while managing costs.