The article presents a comprehensive conceptual and functional analysis of a multi-layer medical network architecture, examining in detail the operating principles of each layer, including the sensing layer, communication layer, and processing and service layer. Particular attention is paid to the scientific analysis of the processes involved in the real-time collection, transmission, storage, and processing of data generated by medical health sensors. In addition, the advantages of cloud-based systems in medical network infrastructures, such as scalability, flexibility, high computational capacity, and centralized management, are comparatively evaluated alongside their limitations, including data privacy concerns, communication latency, cybersecurity risks, and dependency on cloud service providers. Furthermore, the study investigates the structuring of large-scale data collected from medical sensors through cloud-based processing centers, as well as its analysis using artificial intelligence and machine learning techniques. These technologies enable early disease diagnosis, health risk prediction, and the development of personalized treatment plans. The article also demonstrates how cloud-based medical network infrastructures contribute to improving the quality of healthcare services, increasing the accuracy of clinical decision-making, and enabling more efficient management of healthcare resources. Moreover, the principles of establishing dedicated virtual cloud environments for individual patients within the server infrastructure of cloud-based processing centers are thoroughly analyzed. The proposed approach is shown to enhance data privacy and security, strengthen patient control over personal health information, and support the provision of personalized healthcare services (pp.3-15).