Early heart failure (HF) diagnosis using real-world clinical data is hindered by class imbalance, heterogeneous documentation, and differences between primary and secondary care pathways. This study presents a leakage-aware, AI-enabled workflow for synthetic data augmentation to support HF classification across care settings. The framework integrates an IEEE 2801-2022–aligned data quality assessment, synthetic minority-class balancing, virtual population generation, and downstream utility evaluation under cross-validation with strictly real-only validation. Multiple synthetic generation approaches, including oversampling, probabilistic mixture models, and deep generative methods, are systematically compared using distributional fidelity metrics and offline augmentation with XGBoost as a strong tabular baseline. Results demonstrate that moderate synthetic augmentation (40–60\%) improves balanced accuracy and sensitivity in both care settings, whereas excessive augmentation degrades generalization due to synthetic-to-real mismatch. By explicitly separating care pathways and enforcing leakage-free evaluation, this work provides practical guidance for the responsible use of synthetic data in real-world HF decision support.