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We propose CardioTransfer-X, a cross-cohort transfer learning framework within a related clinical benchmark family for tabular CVD risk prediction that yields performance comparable to training from scratch while preserving transparency. Predictive models are pre-trained on a composite multi-hospital heart disease dataset to learn generalized risk patterns, then fine-tuned on the smaller, distributionally distinct Cleveland Heart Disease dataset. To mitigate dataset shift and class imbalance, we introduce a stability-aware, correlation-based feature selection strategy and apply SMOTE only during training. CardioTransfer-X is instantiated with XGBoost, TabNet, and a MLP, enabling systematic comparison across tree-based and neural models. SHAP-based explanations provide global interpretability across architectures, with consistent feature importance rankings before and after fine-tuning. Fine-tuned XGBoost achieves 80.65±4.08% accuracy and 81.74±3.71% F1-score on the Cleveland dataset while maintaining stable source-domain performance, showing performance competitive with training from scratch, while shifting the decision boundary toward higher sensitivity and maintaining clinically meaningful feature attributions.