<p>Due to the rapid development of communication technology, the utilization of Internet of Things (IoT) has increased rapidly. Wireless Sensor Networks (WSNs) play a crucial role in IoT, which is highly applicable to diverse applications. Specifically, energy-efficient routing allows data to be transmitted within the network using less energy. However, existing optimization algorithms often cause unreliable communication due to changes in network conditions. This also limits the performance of IoT-based WSN by leading to high communication delay, reduced network lifetime, and lower throughput performance in dynamic environments. In order to address these challenges, a multi-objective function-based routing scheme is introduced in this research to provide energy-efficient routing and extend network lifetime in IoT-based WSNs. Initially, the group of sensor nodes is divided into clusters to enhance the network lifetime. The best routing path is selected, which is the shortest distance between the base station and the sensor node. Thus, the Cluster Head (CH) is selected by implementing a new Hybrid Reptile Search-Artificial Gorilla Troops Optimization (HRS-AGTO) algorithm to ensure better data communication. Unlike traditional models, the developed HRS-AGTO algorithm iteratively adjusts its position in a large search space to provide optimal CH selection and routing by mimicking hunting behavior. By exploring a broader solution space, the developed HRS-AGTO algorithm identifies near-optimal solutions that enhance performance and enable data transmission across wider network coverage. It is achieved by solving the multi-objective function that considers measures like distance, throughput, latency, energy, and path loss. This multi-objective function is derived by initializing the population in the developed HRS-AGTO algorithm. For each solution, the values in the objective function are calculated by updating them with the fitness function. The process is repeated until the convergence criteria are met to achieve the desired outcome. This objective function is attained with the same HRS-AGTO for determining the best routes to transmit data with minimal energy requirements. In order to get accurate statistical outcomes, the developed model shows 7.6%, 18.3%, 18.1% and 6.8% better than Particle Swarm Optimization (PSO), Jaya Algorithm (JA), Reptile Search Algorithm (RSA) and Artificial Gorilla Troops Optimization (AGTO) in terms of node 150, respectively. Focusing on the network lifetime, the developed model shows 8.06%, 19.1%, 19.03% and 7.17% improved than PSO, JA, RSA and AGTO. Validating energy efficiency, the developed model achieves 0.05%, 0.007%, 0.07% and 0.007% improved than PSO, JA, RSA and AGTO. These performance analyses reveal that the recommended model effectively enhances lifetime of network and energy efficiency during large-scale data transmission in IoT-based WSNs.</p>