<p>Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensors that monitor physical or environmental conditions and transmit data to a base station. In WSNs, context-aware routing leverages environmental or sensor node information to make routing decisions. Traditional routing methods often suffer from inefficient resource utilization, poor performance in dynamic environments, and limited Quality of Service (QoS) support, such as throughput, delay, and packet delivery ratio. To address these issues, this paper introduces the Gaussian Kernelized Clustering-based Multiobjective Crow Search Optimization (GKC-MCSO) technique. This approach enhances context-aware routing by leveraging clustering and optimization. Initially, sensor nodes are randomly deployed. The Gaussian kernelized mean shift clustering technique then partitions the nodes into clusters based on their energy levels. The node with the highest energy in each cluster is designated as the cluster head. Route discovery follows, identifying multiple paths between the source and sink nodes via cluster heads using beacon messages. Multi-objective Crow Search Optimization is then employed to select the optimal path based on criteria such as distance, bandwidth availability, and link connectivity. The effectiveness of the proposed GKC-MCSO technique is simulated using a healthcare dataset. The parameters considered for the performance evaluation are: average packet delivery ratio, packet loss rate, throughput, delay, energy consumption, network lifetime, time complexity, and space complexity. Compared with conventional methods, the proposed GKC-MCSO technique achieves superior performance, enhancing the data delivery ratio and throughput by 5% and 15%, respectively. Moreover, it attains reductions of 24%, 8%, 54%, 12%, 17%, and 13% in energy consumption, network lifetime, packet loss rate, delay, time complexity, and space complexity, respectively, when compared to conventional methods.</p>