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The management, optimization, and security of networks are being drastically changed by the incorporation of software-defined networking (SDN) into medication delivery and healthcare systems. SDN resolves fundamental issues concerning the rapid growth of medical data dynamics, patient monitoring through Internet of Things (IoT) devices, cybersecurity risks, and inefficiencies in the pharmaceutical supply chain by offering consolidated control, programmability, and enhanced flexibility. This chapter analyzes the advanced applications of SDN in healthcare, particularly highlighting blockchain-secured pharmaceutical supply chains, artificial intelligence (AI)-driven network management, and real-time data analytics. One of the innovative features of SDN is context-aware, AI-driven network management, which permits Agile Dynamic Bandwidth Allocation (ADBA) based on real-time monitoring of patient health indicators. SDN improves confidentiality and reduces latency by processing sensitive patient data using edge computing and federated learning at the information's point of origin. In this chapter, SDN models are described with predictive analytics for remote surgery and telemedicine to enhance efficacy. These algorithms predict overpopulation of hospital networks and alter traffic patterns. In addition, the implementation of smart contracts with SDN in blockchains offers a new approach to ensuring drug traceability and fighting counterfeiting, thus enhancing transparency in pharmaceutical logistics. One other important development noted is self-repairing SDN architectures employing AI cyber threat intelligence for real-time automated cyberattack management within hospital networks. Intent-based networking capability permits proactive SDN responses to dynamic cyber threat environments, thus maintaining regulatory compliance with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) in real-time. This chapter also examines how SDN might integrate with multi-access edge computing (MEC), thereby significantly improving real-time monitoring of important patients through IoT-enabled medical devices. The dynamic optimization of networks ensures the effective transmission of genetic data for specific treatment protocols, thereby improving tailored medicine approaches. Despite these advances, challenges of SDN adoption in the healthcare sector still revolve around controller limitations, backing compatibility with legacy systems, and the need for quantum-tough, threat-resilient encryption, shielding sensitive medical information from prying eyes. To get around these limitations, the chapter provides innovative solutions such as hybrid SDN models that combine 5G network slicing, zero-trust security systems, and AI-assisted fault tolerance techniques. Future advancements include bio-digital twin models, neuromorphic computing, and the integration of SDN with sixth-generation (6G) networks. This integration will enable high-performance distributed networks to simulate real-time molecular-level drug interactions. Healthcare system administration may become autonomous through the integration of decentralized AI-driven SDN controllers. With this advancement, overall efficiency would increase and reliance on human involvement would decrease. The final section of this chapter presents an optimistic assessment of SDN transformative potential in the domains of healthcare and medication distribution. With the use of cutting-edge technologies like edge computing, blockchain, and AI, SDN has the potential to transform patient-centered healthcare systems. This will ensure that the upcoming medical innovations are safer, more effective, and more precise.