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This study proposes an interpretable and high-accuracy ensemble learning framework for predicting aspartate aminotransferase (AST) levels using open-access biomedical datasets. Using a structured pipeline of preprocessing, feature selection, and model ensembling, we evaluated a series of regression algorithms including Random Forest, XGBoost, CatBoost, and three stacking architectures. The best-performing ensemble (Stacking_v2) achieved R² = 0.98 and RMSE = 1.23 on the validation set, surpassing conventional and single-model approaches. Feature importance was assessed using SHAP values, mutual information, and correlation analysis, revealing that gamma-glutamyl transferase, ferritin, and anthropometric markers had the greatest predictive impact. The proposed stacking-based model demonstrates excellent generalization, robust calibration, and high interpretability, and can serve as a benchmark for algorithmic evaluation in medical data modeling. The work highlights the effectiveness of ensemble regression and interpretable AI in real-world clinical prediction tasks using routine biomarkers.