The growing demand for intelligent healthcare systems emphasizes the need for advanced technologies that support continuous, non-intrusive patient monitoring in clinical environments. This paper presents a comprehensive solution that integrates both static and mobile IoT nodes, coupled with AI-driven perception and analytics, to enhance monitoring capabilities within hospital settings. The proposed system incorporates three core components: autonomous navigation, computer vision–based health assessment, and machine learning–enabled sensor analysis. Static nodes are deployed at each patient’s bedside to collect physiological and environmental data in real time using embedded sensors, while simultaneously capturing visual data through fixed cameras. Sensor data is analyzed using machine learning models that predict missing values in case of sensor malfunction or loss, ensuring reliable and uninterrupted monitoring. In addition to static setup, a mobile robot navigates autonomously through hospital wards, executing monitoring routines based on user-defined schedules or clinical priorities. During its rounds, the robot collects visual data of patients and surgical sites. Deep learning models, applied to extract subjective information, are employed to analyze patient posture, wound healing progress, and to detect essential items such as medicine containers and water bottles. These models also support autonomous navigation by enabling path recognition and action planning. The system integrates insights from both static and mobile nodes into structured, real-time reports that assist in the early detection of clinical issues and timely informed medical decisions. This solution provides a scalable and objective approach to continuous patient assessment. It improves monitoring quality services, reduces reliance on subjective manual evaluations, enhances staff efficiency, and fosters a more responsive, data-informed hospital environment.