The 5G-enabled Wireless Sensor Networks (WSN) use the increased capabilities of 5G technology to represent the next version of conventional WSN environments. WSN performance may be affected by interference from high-density 5 G networks. The novel Hummingbird-based Graph Bernoulli Binomial Trust Management Network (HBbGBBTMN) proposed in this research is to enhance smart, secure, and energy-saving routing in 5G-enabled wireless networks. Python is initially used to simulate and model the network under consideration, accounting for fluctuating network conditions and dynamic node dynamics. An improved Hummingbird algorithm is used to detect and remove nodes with high energy consumption, thereby minimizing routing inefficiencies and avoiding suspicious behavior. To protect data integrity and prevent route disruption, malicious nodes are continuously detected and removed. The trust of the remaining nodes is calculated using the Bernoulli-Binomial distribution, which estimates each node's trust based on its previous packet-forwarding history. Such trust mechanisms are combined with node energy levels as well as network dynamics to form a fitness function that identifies optimal routing patterns. The suggested system is validated through extensive performance analysis, including measurements of packet delivery ratio, throughput, packet drop rate, delay, and malicious node prediction accuracy. The findings indicate that in decentralized wireless environments, HBbGBBTMN significantly enhances network efficiency, security, and dependability.