<p>Integration of Wireless Sensor Networks (WSN) with Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Healthcare Things (IoHT) results in smarter, more responsive, and energy-efficient systems across various domains. To ensure system sustainability in remote and inaccessible locations, clustering techniques are widely adopted in WSNs to optimize energy utilization and enhance the network's operational lifetime under stringent resource constraints. But the selection of optimal Cluster Heads (CHs) in a complex search space regarding a dynamic environment is still a challenging task. The present work addresses the important issue of energy efficiency in WSNs and focuses on the optimal selection of CHs to enhance network longevity. A novel algorithm called Jackbee Optimization (JBO) has been proposed, which integrates the decision-making behaviour of bees with the situation-aware behaviour of jackdaws to expedite the decision-making process. The algorithm utilizes a multi-objective function to determine the optimal CHs and maintains a good exploration–exploitation balance. The Mobile Sink (MS) is taken into consideration in this study, which follows a predefined path and Rendezvous Node (RN) is chosen using the JBO algorithm in such a way to assure the energy efficiency in the network, along with minimal data delivery or greater throughput in the network. The simulation results demonstrate improved performance across various performance metrics, such as normalized energy, number of alive nodes, end-to-end delay, and throughput, which underscores the effectiveness of the proposed algorithm. The proposed JBO-WSN algorithm demonstrates considerable performance enhancements over other methods in terms of the number of active nodes 60.9195%, end-to-end delay 88.0346%, normalized energy consumption 68.9319%, and throughput 25% better than the SSA-WSN model.</p>