Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3751 2018 2018 Research Scholar, Department of Computer Science and Engineering , FEAT, Annamalai University, Chidambaram, India R. R. Associate Professor ,Department of Computer Science and Engineering , FEAT, Annamalai University, Chidambaram, India N. Suguna The Internet of Things (IoT) devices and technologies are more effective in the medical sector. It includes the combination of numerous interrelated sensor, systems, and devices for gathering, examining, and conveying health-related information for medicinal uses. In the healthcare field, Blockchain (BC) technology embraces huge latent for increasing the security and confidentiality of data. BC-aided intrusion detection on IoT healthcare methods is a new technique for increasing the privacy and security of complex health data. Patients have superior control throughout their information’s growth, granting or revoking access as needed, but healthcare employees will modernize data sharing and certify the reliability of significant data. On the other hand, deep learning (DL) is excellent for transforming healthcare analytics, presenting rapid and tremendously precise estimations of medicinal circumstances. This paper presents a Blockchain-Assisted Deep Learning Model for Enhancing Healthcare Data Security with Metaheuristic Optimization Techniques (BCDL-HDSMOT) model. The main intention of the BCDL-HDSMOT technique is to develop an effective method for enhancing data security in the medical sector. At first, the blockchain technique is applied in healthcare to enhance data security, interoperability, and transparency while ensuring patient privacy and efficient record management. Next, the data pre-processing stage employs min-max normalization to clean, transform, and organize input data into a suitable quality for analysis. Besides, the black widow optimization algorithm (BWOA) has been deployed for the feature selection process to select the relevant features from input data. For the classification process, the proposed BCDL-HDSMOT technique designs a versatile long-short-term memory (VLSTM) method. At last, the improved seagull optimization algorithm (ISOA)--based hyperparameter selection process is performed to optimize the classification results of the VLSTM method. The experimental evaluation of the BCDL-HDSMOT algorithm can be tested on a benchmark dataset. The wide-ranging outcomes highlight the significant solution of the BCDL-HDSMOT approach to the cyberattack detection process. 2025 2025 402 417 10.54216/FPA.190229 https://www.americaspg.com/articleinfo/3/show/3751