<p>Cognitive radio technology has resolved spectrum problems in Wireless Sensor Networks. In this Paradigm, primary users use licensed bands, whereas secondary users use these channels opportunistically. Cluster-based routing techniques offer energy economy and scalability in Cognitive Radio Sensor Networks. These protocols use Cluster Head nodes to collect and transmit data to the sink. Early Cluster Head node loss leads to frequent re-clustering, putting network stability and performance at risk. Traditional methods for choosing Cluster Heads often fail to consider Cluster Head energy usage, leading to quick fails and uneven cluster distribution. In order to solve the problem, a selection parameter is proposed in this work which Cluster Head selection algorithm utilizes the Whale Optimization Technique that is stable with respect to those parameters. The proposed approach uses the Whale Optimization Technique as a meta-heuristic algorithm to select Cluster Heads with the network-coverage-based optimization, energy-efficient clustering and equal-sized clustering. Whale Optimization algorithm leverages spiral foraging as well as bubble-net hunting like humpback whales to augment selection of Cluster Heads. A number of simulation experiments for the proposed method are also conducted using Network Simulator (NS-2). Due to the stable Cluster Head selection &amp; efficient data aggregation among the sensor nodes, the experimental shows that the proposed method significantly improves energy consumption by 20%, end to end delay by 10%, overhead by 12%, throughput by 15% respectively.</p>