<p>The complexity and cost of contemporary healthcare systems are well-known. Better health record management with advanced technologies needs for mitigating the complex medical systems. Innovation in healthcare delivery with Electronic Health Records (EHR) sharing system is preferred. Throughout the information exchange process, blockchain technology can shield data from hackers. Several traditional methods exist to safeguard electronic medical records. This research aims to propose a novel method in electronic health record security analysis based on quantum cryptography with blockchain models and machine learning techniques. The patient health record data has been analyzed to detect malicious data using a convolutional adversarial transfer encoder neural network. The classification output gives the malicious data, and then the network security is enhanced using quantum cryptography integrated with a federated blockchain algorithm. Experimental analysis is carried out for various EHR datasets regarding training accuracy, mean square error, average precision, data privacy rate, and network security rate. The proposed technique provides a training accuracy of 98%, average precision of 94%, data privacy rate of 93%, Network Security Rate of 95%, and Mean Square Error of 47%. We demonstrate the accuracy and forward security of solutions in EHR scenarios, and our scheme’s efficiency compared to other existing schemes is shown by the thorough performance evaluation.</p>