<p>Mobile healthcare systems increasingly require intelligent, AI-driven mechanisms to deliver personalized and reliable services within dynamic and heterogeneous environments. The current research paper in-troduces PRISM-HS, an Intelligent and Ontology-Driven AI Framework for Personalized Mobile Healthcare, designed for intelligent service com-position and real-time adaptation. PRISM-HS leverages an OntoUML-based conceptual model enhanced with the SNOMED CT medical ontology to formally represent users’ profiles, contextual parameters, and service semantics. Through integrating Artificial Intelligence techniques, including Natural Language Processing (NLP) and semantic reasoning, the framework transforms unstructured user inputs into formal representations and ensures that generated workflows are contextually relevant and clinically valid. For execution, PRISM-HS adopts Business Process Execution Language (BPEL), providing a standardized orchestration layer that converts semantically validated requests into deployable healthcare processes. Designed for pervasive and mobile computing environments, PRISM-HS dynamically adapts to patients’ status, device constraints, and environmental conditions. Evaluation across multiple realistic healthcare scenarios revealed that PRISM-HS outperforms state-of-the-art approaches in terms of precision, recall, execution time, and success rate, achieving a high personalization accuracy (91%) and a powerful adaptability. This work advances context-aware and AI-driven computing for personalized mobile healthcare by offering a scalable, ontology-based, and BPEL-compliant framework for secure and adaptive service composition.</p>