Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2086 2019 2019 Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq Raaid Alubady Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Rawan A A.shlaka Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Hussein Alaa Diame Computer Technologies Engineering, Al-Turath University College, Baghdad,Iraq Sarah Ali Abdulkareem Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Ragheed Hussam Department of Applied Data Science, Noroff University College, Kristiansand, Norway Sahar Yassine Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India. Venkatesan Rajinikanth The confidentiality of electronic medical records (E-Medical records) is of the utmost importance. Consequently, healthcare companies are responsible for ensuring their patients' medical records' privacy, security, and service management. Innovative agreements will ensure patient satisfaction for management. The study's primary goals are to enhance data security service management and reduce the amount of external involvement with healthcare data. This study explores a novel approach to improve the security and confidentiality of e-medical information by examining the feasibility of utilizing the blockchain system within the context of the IoMT (Internet of Medical Things). The medical care management platform uses blockchain technology to manage e-health records effectively. This paper presents a paradigm for e-medical record services based on IoMT resources, which integrates blockchain technology with Secure Federated Learning (BT-SFL-IoMT). The data is stored on blockchain, and predictions and analyses are made using secure federated learning. Hyper ledger Analyzer is used to assess the latency and speed of blockchain transactions and capture access activity and authorization events. As verified by the results, the functionality is resistant to unauthorized retrievals and fits the needs of real-world settings while securing e-medical records. Many metrics, including testing accuracy of federated learning, Convergence speed, and Performance analysis of the proposed model, demonstrate its efficient use in secure databases. 2023 2023 86 100 10.54216/JISIoT.080207 https://www.americaspg.com/articleinfo/18/show/2086