Wireless Sensor Networks (WSNs) and their applications require energy-efficient communication, with limited resources of node such as storage, computing, battery power, and communication, data processing, aggregation, and clustering are energy-intensive, and deployment and discovery are complicated in WSN. This study uses bacterial foraging optimization and ant colony optimization (BFOACO) to find suitable gateways (FCHs), cluster heads (CH), and route from SNs to base stations (BS). Long-term network utilization requires balancing node energy usage with CHs and FCHs. Ant colony optimization dynamically finds the energy-aware route with lower network overhead. The algorithm assesses node energy consumption, network lifespan, packet delivery ratio (PDR), and throughput. Node energy usage (9.53 mJ), PDR (97.5%), and network lifetime (9920 rounds) are WSN performance outcomes for 300 sensors. The proposed system operates with 94.24% accuracy. The BFOACO clustering and routing algorithm outperforms over parallel research algorithms.