Deep learning has transformed AI-driven healthcare services through advanced medical imaging analysis, yet privacy concerns over protected health information persistently hinder data sharing in clinical service ecosystems. Conventional anonymization methods, while compliant with healthcare data governance standards, irreversibly degrade diagnostic utility through quality loss or incomplete de-identification. We propose DP-UOTM, a differentially private Unbalanced Optimal Transport-based framework for medical service systems, generating regulatory-compliant synthetic images that preserve diagnostic fidelity via optimal transport, enforce provable ( \(\epsilon \) , \(\delta \) )-DP guarantees against re-identification, and resolve class imbalances through label distribution calibration. Evaluated on benchmarks MNIST and Fashion-MNIST, DP-UOTM outperforms DP-GANs with \(\ge \) 22% lower FID scores and \(\ge \) 2% higher classification accuracy across privacy budgets. Clinical validation on chest X-ray services demonstrates synthetic data maintains \(\ge \) 4% diagnostic accuracy improvement over raw data through class balancing. By enabling privacy-preserving substitutes for raw medical images, this framework directly supports federated healthcare services and multi-institutional collaborations while addressing critical needs in clinical AI service development—providing scalable, audit-ready synthetic datasets that comply with medical data regulations without compromising diagnostic utility, thereby bridging the gap between AI innovation and healthcare privacy requirements.