###### Abstract
The size of the medical industry's digital twin market was estimated at USD 1.41 billion in 2023 and is projected to expand at a compound annual growth rate of 40.01% to reach USD 28.88 billion by 2032, according to SNS Insider Research [23]. To obtain a good digital representation of a patient, DT can gather and analyze data about them from a variety of sources, including wearables, medical devices, and electronic health records. Distinctive treatment plans, precision assessment, and real-time monitoring become feasible through this, enhancing patient outcomes from medical care. With the use of machine learning algorithms, DT can predict the progression of diseases, identify individuals at a higher risk, and suggest ways to avoid them. Consequently, this prevention strategy improves patient safety, long-term results, and the distribution of resources across medical systems. Additionally, it allows professionals to collaborate by facilitating information sharing and enhancing coordination. Additionally, they offer simulation and training programs that let medical professionals home their abilities in a virtual environment [24; 25; 26]. Even though DT technology has so much potential, there are still several issues to be solved, such as data privacy and security assurances. For medical services, further research and investment are essential to fully exploit all benefits. Federated learning (FL) is one of the emerging fields in artificial intelligence that revolves around the concept of decentralized data. However, due to the frequent occurrence of cyberattacks in real-time applications, many industry professionals are hesitant to embrace Internet of Everything (IoE) technology [27]. When integrated with new DT technologies in health services, FL is an effective method of artificial intelligence that hasenormous potential. This combination could reduce privacy concerns, increase telemedicine accuracy, and improve machine learning forecasts and efficiency. Furthermore, it might make it possible for real-time monitoring across several medical sectors, including intelligent patient care platforms, personalized medicine, and informed prediction. When FL and DT are combined, local patient data are generated, increasing personal security and encouraging the widespread use of DT in clinical systems. On the other hand, blockchain has emerged as a notable solution for mitigating cyberattacks in medical systems, effectively addressing various threats by ensuring data integrity, security, and transparency. However, despite its successes, blockchain technology has some limitations when applied in practice [28]. So, blockchain can complement FL by providing a decentralized, immutable ledger to track and verify model updates, enhancing data integrity and security. By combining these two technologies, medical networks could ensure that the FL process is transparent, tamper-proof, and traceable, particularly in multi-party collaborations across institutions and nations. This could foster greater trust and accountability in medical data sharing, particularly in sensitive areas like clinical trials and patient record aggregation. Blockchain can be leveraged to create decentralized and secure healthcare-based digital twin systems that operate autonomously while ensuring robust security and resilience [29]. In essence, the integration of DT technology, FL, and blockchain offers a synergistic approach to developing intelligent and secure systems. By combining these advanced technologies, it becomes possible to design solutions that optimize performance, predict and prevent potential issues, and function autonomously with enhanced security, transparency, and reliability. In summary, this paper presents a comprehensive review of the utilization of healthcare-based digital twins with blockchain and FL for advanced secure medical applications.