Sensor networks are the backbone of emerging Internet of Things (IoT) ecosystems, serving as critical components in various applications ranging from environmental monitoring to smart cities. However, the Quality of Service (QoS) in sensor networks is compromised by issues such as communication delay, energy consumption, and throughput limitations. To address these challenges, this paper introduces a novel optimization algorithm called Ant Lion Grey Wolf Optimizer (ALGWO). Inspired by the foraging behaviour of ant lions and the hunting strategies of grey wolves, ALGWO aims to optimize both temporal and spatial routing in sensor networks. It uses spatial node features, and temporal network parameters to optimize routing performance under real-time scenarios. The algorithm deploys bioinspired techniques for iterative search and congestion control mechanisms to enhance network performance. Our experiments demonstrate remarkable improvements in QoS parameters: an 8.3% reduction in communication delay, a 4.9% decrease in energy consumption, and a 10.4% boost in throughput when compared to recently proposed routing models. The findings indicate that ALGWO provides a robust and efficient framework for optimizing sensor network performance, holding significant promise for real-world applications.