Wireless sensor networks (WSNs) have a limited battery powered sensor which are spatially dispersed and endlessly gather data associated with the nearby environments. Edge and fog computing provide better bandwidth efficiency than cloud computing due to it process data outside the cloud which results in minimal bandwidth. However, choosing reliable and energy-efficient paths is challenging while ensuring secure communication which results in suboptimal performance. This research proposes Density Factor of Quasi-Cosine-Honey Badger Algorithm (DFQC-HBA) for trust and energy-efficient clustering and routing in WSN using edge-fog computing. The DFQC-HBA optimize routing by considering node density which provides energy efficient. The proposed DFQC-HBA increase network performance by avoiding malicious nodes which enhance both reliability and throughput. The trust, intra-cluster distance, and node degree are used as a fitness function to select secure cluster head (SCH). In routing, the distance and energy are considered to assist in optimizing path selection which minimize energy consumption and delays. Hence, the proposed DFQC-HBA achieves a less energy consumption of 17.65 mJ for 200 rounds compared to Sunflower Optimization Approach (SOA) and Firefly Approach (FA), respectively.