Strong privacy features that safeguard patient information are required with the growing dependency of healthcare systems on APIs. AI-based API security in health systems is explored in this study, taking threat mitigation and real-time anomaly detection into consideration. Machine learning techniques were utilized to detect probable security threats like unauthorized access and breaches. MIMIC-III and MIMIC-IV databases were used to emulate API interaction with electronic health records (EHRs). The approach combines AI-based intrusion detection with Mulesoft’s in-built functionalities like OAuth, JWT validation, and rate limiting. With reduced false positives in monitoring, results show that AI-based anomaly detection significantly boosts threat detection. The study illustrates the viability of information security enhancement in contemporary healthcare systems and the efficacy of AI-based security in API management.