The Rational Unified Process (RUP) is an iterative software engineering methodology that ensures structured alignment between engineers, stakeholders, and regulatory entities by breaking down development into phased, well-defined cycles. It facilitates adaptability within predefined constraints, ensuring that evolving requirements are incorporated efficiently. In distributed systems, the adoption of microservices architecture significantly enhances scalability, maintainability, and system modularity. Unlike monolithic architectures, where tightly coupled components create complexity and deployment challenges, microservices assign distinct responsibilities to independently deployable services. This modular design simplifies development, integration, and version control, enabling teams to enhance specific system functionalities without disrupting the entire platform. Furthermore, inter-service communication mechanisms, such as RESTful APIs and event-driven architectures, optimize system interoperability and real-time responsiveness. Medical applications are essential in disease prevention, treatment personalization, and long-term health management. However, their effectiveness relies on ensuring that they are adaptable to diverse patient demographics. Patients differ not only in medical conditions but also in cognitive abilities, technological familiarity, and accessibility requirements. Thus, a user-centric development approach is critical to ensuring that such systems are inclusive, intuitive, and supportive of patient needs. This chapter presents the design and implementation of an AI-powered medical application utilizing the RUP framework and microservices-based deployment model. The system development process prioritizes patient-centered requirements, ensuring that AI-driven functionalities evolve through continuous iteration, stakeholder feedback, and rigorous validation cycles. By integrating machine learning models for disease risk assessment, real-time decision support, and personalized health recommendations, the proposed system fosters adaptability, accessibility, and evidence-based healthcare optimization. The iterative nature of RUP ensures that the platform remains scalable, regulatory-compliant, and future-proof, maximizing usability and long-term impact for both patients and healthcare professionals.