The increasing use of Internet of Medical Things (IoMT) devices in the e-Healthcare system is leading to a rise in cyberattacks. In the current cloud computing landscape, organizations are shifting from local infrastructure to cloud networks, which raises concerns about cloud security because sensitive data is stored there. Traditional cloud security solutions can suffer from centralized data vulnerabilities, rigid security policies, and inadequate access controls, leaving businesses vulnerable to new attacks. Attacks on the Dark Web have become more sophisticated, concentrating on cloud settings through a variety of techniques. While 6G is still in its early phases of development, the integration of its encrypted communications and decentralized computing will significantly improve digital healthcare systems. SplitFed Learning (SFL) can address data privacy and resource limitations in edge-based Internet of Things (IoT) systems, whereas Traditional approaches struggle with sequential data, which is frequently present in healthcare data streams. This paper offers a conceptual security framework that integrates AI-Driven Security Policy Management, SplitFed Learning, Blockchain technology, Digital Twin technology, and Zero Trust Network Access (ZTNA) principles to address these challenges.