In this work, we propose Temporal Extended (TE)-CTABGAN+, a unified GAN framework for generating synthetic Electronic Health Records(EHRs) that encompass continuous, categorical, and temporal variables. Ourframework makes six key contributions: (1) develop a Noise Injection Pipeline (NIP) to introduce controlled variability into synthetic data; (2) utilize CTABGAN+ to produce high-fidelity synthetic EHR data, comprising both continuous and categorical variables, from NIP output; (3) design a Temporal Sequence Processor (TSP) to preserve temporal integrity in EHRs; (4) integrate TimeGAN to capture temporal dependencies in EHR data; (5) conduct extensive experiments on Synthea and MIMIC-IV datasets, evaluating our framework’s fidelity and coherence using statistical metrics, including Kolmogorov–Smirnov (KS), Jensen-Shannon Divergence (JSD), and Chi-Square (CS); and (6) perform a domain expert evaluation with a medical doctor and a healthcare professional, quantifying their ability to distinguish between real and synthetic EHRs using Accuracy, Precision, Negative Predictive Value, Specificity, and Matthews Correlation Coefficient. Our results demonstrate TE-CTABGAN+’s superior performance, achieving the lowest KS, JSD, and CS. The domain expert evaluation shows that both experts struggle to distinguish between real and synthetic EHRs, with performance approximating random chance. These findings indicate that TE-CTABGAN+ generates highly realistic synthetic EHRs, preserving both temporal and non-temporal coherence. Our framework has the potential to facilitate the creation of high-quality synthetic EHR datasets, supporting various healthcare applications while maintaining patient data confidentiality.