This research introduces the Race-Stratified Ensemble Framework (RSEF) to address accuracy and fairness in survival prediction for patients undergoing hematopoietic stem cell transplantation (HCT). Despite HCT’s critical role in treating hematologic malignancies, racial disparities in prognosis persist, with traditional models overlooking fairness. RSEF integrates fairness through a four-stage architecture: data preparation and stratification, base model training, direct model optimization, and metamodel ensemble with risk score calibration. Key innovations include a dual-objective transformation strategy, race-specific data processing, data auditing, a multi-model ensemble, and race-specific risk score calibration. Experiments on the CIBMTR dataset show that RSEF achieves a stratified concordance index of 0.6918, improving performance by 1.5 percentage points and reducing racial group variability by 50%. Performance gains are most notable among African American and Hispanic patients, demonstrating RSEF’s effectiveness in reducing racial bias in medical predictions. This study offers an equitable HCT survival prediction method and new insights into algorithmic fairness in medical AI, potentially advancing fairness in precision medicine.