The rapid digitization of clinical records has necessitated a transition from static Health Information Exchange (HIE) to dynamic, intelligent interoperability. However, the pursuit of data liquidity frequently conflicts with the imperatives of patient privacy and cybersecurity. This paper proposes a tripartite framework that integrates Artificial Intelligence (AI), Zero Trust Architecture (ZTA), and Federated Learning (FL) to facilitate secure and seamless data exchange across heterogeneous healthcare environments. By utilizing Natural Language Processing (NLP) for semantic mapping and Deep Learning for real-time anomaly detection, the proposed system transcends the limitations of legacy standards such as HL7 v2 and early FHIR iterations. We introduce the Intelligent Interoperability Index (III), which is a novel quantitative metric designed to evaluate system performance across semantic accuracy, latency, and security resilience. Our findings, supported by simulation data and a review of state-of-the-art implementations, suggest that AI-driven orchestration can improve diagnostic precision while reducing risk of unauthorized data egress by up 20%. Further, the integration of blockchain technology yields an immutable audit trail to ensure regulatory compliance with HIPAA and GDPR. This research offers a strategic roadmap for institutional adoption, emphasizing the economic feasibility and operational Return on Investment (ROI) of intelligent HIE systems.