The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.