###### Abstract
By addressing these objectives, this aims to contribute to the understanding and advancement of healthcare-based digital twins, shedding light on their potential applications and providing insights into their development and future directions. While prior studies have explored the integration of new technology in healthcare, they have also overlooked its unique challenges. Few works systematically address security, scalability, and future research directions, and there is no prior work covering the integration of FL, blockchain, and digital twin in healthcare systems. This gap underscores the need for a comprehensive review that unites blockchain, FL, and DT to advance next-generation healthcare solutions. The selected research papers were sourced from different publishers such as Springer, Elsevier, MDPI, IEEE Xplore, and other databases, as depicted in Figure 1, using systematic searches. The keywords used were as follows: healthcare, bioengineering, blockchain, federated learning, healthcare security, AI, IoT systems, and digital twins. The selection criteria were peer-reviewed status, publication within the last decade, and relevance to the study's focus, i.e., leveraging blockchain and federated learning in biomedical and digital twins, thus ensuring a diverse and impactful range of contributions to this evolving field. In summary, the survey aimed to realize the following objectives:  
* Investigation of Healthcare, Digital Twins, Blockchain and Federated Learning: This paper explores the concepts and terminologies related to healthcare, digital twins, blockchain, and federated learning, providing a clear understanding of these foundational concepts. * Exploration of Healthcare-based Digital Twins: This study delves into the concept, architecture, and components of healthcare-based digital twins, examining their applications. * Investigation of Healthcare-based Digital Twins with Federated Learning: This study delves into the concept of federated learning in healthcare-based digital twins, examining their application in EEG. * Exploring Blockchain in Healthcare-Based Digital Twins: This study examines the integration of blockchain technology within healthcare and digital twins. * Highlighting Development Platforms and Solutions: This paper highlights recent development platforms and solutions available for the implementation of healthcare-based digital twins, showcasing the tools and technologies that enable their creation and integration. * Exploration of Challenges, Opportunities, and Future Trends: This study identifies and examines the challenges associated with healthcare-based digital twins, while also discussing opportunities and future trends for further advancements in this field. The rest of this paper is laid out as follows: Section 2 introduces an overview of the fundamental concepts and principles of digital twins, blockchain, and federated learning. Section 3 presents healthcare with digital twins while platforms and solutions are presented in Section 4. Section 5 presents a case study on federated learning for electroencephalogram (EEG) signal classification, and Section 6 provides tools for the deployment of federated learning models. Section 7 presents real-world federated learning (FL) healthcare projects, and blockchain-based DT is presented in Section 8. The challenges, opportunities, limitations, and open research directions in the paper's subject are presented in Section 9. Finally, the conclusion to this innovative theme is presented in Section 10.