<p>Diabetes is a widespread health issue that requires regular monitoring and professional attention for proper control. Combining IoT technology with traditional healthcare systems has enhanced the efficiency and quality of medical care. The sensor data is sensitive and may contain highly confidential information, such as medical diagnoses, clinical records, vital signs, and patient health data. Various privacy-preserving methods are currently used in disease prediction systems. However, patients may still be at risk of multiple health conditions. This study proposes a blockchain-integrated deep learning framework to enable secure data transmission within an IoT-based healthcare framework. Firstly, the medical data is gathered from IoT sensors, and then encrypted using the Sand Cat Optimized Elliptic Curve Cryptography (SOECC), where the optimal key is generated using Gaussian Perturbation and Brownian motion centered Sand Cat Optimization (GBSCO). Then, the encrypted data is stored in the blockchain, and the user decrypts it using the optimal private key. The disease prediction stage is implemented, where preprocessing is first performed to improve the dataset quality. After that, the dimensionality of features is reduced by using linear discriminant analysis (LDA). Lastly, classification is performed using the Optimized bidirectional gated recurrent unit (OBGRU). The outcomes demonstrate that the proposed framework achieves faster encryption and decryption processes, accurately determines a patient’s health condition with 99.15% accuracy, and demonstrates greater reliability than current advanced methods.</p>