Robot-assisted surgery (RAS) has witnessed a rapid expansion in recent years, further accelerated by the emergence of Healthcare 5.0 and its enabling technologies, particularly artificial intelligence. These advancements have significantly improved critical areas such as surgical training, image segmentation, and classification. Despite this growing adoption, healthcare institutions still lack a structured and comprehensive roadmap (RM) to guide the implementation and operational management of RAS (OM-RAS). This study aims to develop a robust RM to support healthcare facilities in effectively integrating RAS into clinical practice. Two main research questions are addressed: (1) What are the essential requirements for successful RAS implementation? and (2) What defines a practical and reliable framework for managing RAS operations? The contributions of this study are twofold: first, it offers a structured seven-theme roadmap for OM-RAS that centers technology and infrastructure (segmentation, kinematic modeling, AI tools, advanced control), links these with implementation, ethics–legal, and training/HR, and specifies initial integration steps aligned with Healthcare 4.0/5.0 (telesurgery, 6G, integrated AI, cybersecurity). Second, it sets an analytics-driven agenda that quantifies inter-theme dependencies to forecast workflow variability and optimize scheduling, resource allocation, and team assignment under real-world constraints.