Abstract: Background Bloodstream infection (BSI) represents a leading cause of morbidity and mortality in hemodialysis populations, with in-hospital mortality rates reaching 50%. Conventional clinical severity scoring systems (qSOFA, APACHE II) demonstrate inadequate predictive accuracy in dialysis cohorts, failing to identify 20-40% of high-risk patients at presentation. Machine learning with explainable artificial intelligence (SHAP) offers potential to improve mortality prediction while maintaining clinical interpretability. Methods This retrospective cohort study analyzed 148 hemodialysis patients with confirmed BSI from a tertiary dialysis center (2018-2022). Five machine learning algorithms were developed and validated using 5-fold stratified cross-validation: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine. SHAP-derived feature importance analysis identified patient-specific and population-level mortality drivers. Performance was compared against qSOFA and APACHE II using AUROC as the primary metric. Results The cohort demonstrated 50% in-hospital mortality (n=74 deaths). Random Forest achieved superior discriminative ability with AUROC 0.94 (95% CI 0.89–0.98), representing 23-percentage-point improvement over qSOFA (AUROC 0.71, p<0.001) and 18-percentage-point improvement over APACHE II (AUROC 0.76, p<0.001). Cross-validation demonstrated robust generalization (mean AUROC 0.945±0.013, coefficient of variation 1.4%). SHAP analysis identified septic shock (SHAP 0.487) and mechanical ventilation (SHAP 0.412) as dominant mortality predictors. Notably, chronic neurological disease independently amplified mortality risk 3.91-fold but is not captured by traditional scoring systems. Top 10 features ranked identically across all four algorithms, validating finding robustness. Conclusions Machine learning with SHAP-based interpretability substantially improves mortality prediction accuracy in hemodialysis BSI compared with conventional clinical scores. SHAP transparency enables identification of patient-specific risk drivers and novel mortality associations, facilitating personalized clinical decision-making. A clinical integration framework demonstrating real-time risk reassessment as clinical status evolves is proposed. Prospective validation studies should assess whether SHAP-guided risk stratification improves patient outcomes compared with conventional management.