Modern communication networks have been completely transformed by the Internet of Things (IoT), which makes it possible for a wide range of devices to exchange data efficiently. But maintaining the best possible Quality of Service (QoS) in IoT networks—especially in cluster-based architectures—remains a major difficulty. In order to improve QoS metrics in IoT routing, this study presents the Deep Belief Lion Optimization (DBLO) algorithm, a novel combination of the Lion Optimization and Deep Belief Networks (DBN). Modern techniques such as Ant Colony Optimization (ACO), Krill Herd Algorithm, and Convolutional Lion Routing Optimization (CLRO), which blends LOA with Convolutional Neural Networks (CNN), are compared to the DBLO algorithm. Critical QoS metrics, including throughput, energy consumption, routing overhead, packet delivery ratio (PDR), and end-to-end delay, are used to gauge performance. Results from experiments show how effective the DBLO algorithm is at maximizing network performance, cutting down on energy use, and guaranteeing dependable data delivery. The suggested method opens the door for more effective and scalable IoT networks by providing a solid solution for QoS improvement in IoT.