<p>Wireless Body Area Sensor Networks have changed how patient monitoring is done through the integration of the Internet of Things (IoT) into healthcare. These networks which include wearable and implantable devices allow constant health tracking, especially in regard to chronic illnesses and urgent situations. However, the future of Wireless Body Area Sensor Networks (WBASNs) is evolutionary with very major issues such as secure transmission of data and management of health information. Although traditional cryptographic algorithms can be valuable in most general systems, they cannot be effectively used in WBASNs because the latter systems possess the following characteristics: restricted computational power, memory capacity, and battery lifetime. Some of these problems can be solved by lightweight cryptographic techniques, but generally, they do not offer the dynamic access control that is crucial in multiuser healthcare premises. In order to close these gaps, this research puts forward the concept of a Hybrid Lightweight Cryptographic Algorithm accompanying Attribute-Based Encryption (HLCA-ABE). At the same time, the proposed framework encompasses AES for data encryption, ECC for key management, and ABE for attribute-based dynamic access control. The analysis shows it can better manage concerns related to confidentiality, access control, and resource utilization in IoT Health Care contexts. This study proposes a secure data transmission framework using the Cat Hunting Optimization Algorithm and a Residual Group Attention Network with Depthwise Separable Convolutional Neural Networks (RGA-DSCNN) for health monitoring. Additionally, Mountaineering Team-Based Optimization refines the RGA-DSCNN, enhancing performance by optimizing key parameters to ensure robust and efficient healthcare monitoring. The introduced approach attains higher accuracy at 99.9%.</p>