The Internet of Things (IoT) has a wide range of uses today, including weather systems, traffic management, intelligent infrastructure, security systems, and more, that generates a lot of data that provides issues like control clustering and routing protocols. It addresses the management of data transfers. In order to improve the energy efficiency of the network, various mechanisms like energy-based clustering and sleep schedules are incorporated into routing protocols because, in general, IoT devices have limited performance in terms of battery life and energy efficiency. The majority of designs are complex and difficult to implement in real-world situations, according to current research. In this regard, this study proposes a clustering algorithm-based routing technique. By identifying both the clusters and the network, the proposed strategy first applies the Improved_African_Vulture optimization algorithm (IAVOA) clustering technique to group the members of the network. The multi-objective particle swarm evolution (MOPSO) algorithm then selects the interface nodes to send packets to the base station as the routing time increases.  The proposed strategy is evaluated with the help of the MATLAB programming language. The total energy consumption, normalized average energy and residual energy of proposed system are used to calculate the evaluations' outcomes, which are then presented in different distinct scenarios. The evaluations show that the proposed approach performed better than the comparable approach in both evaluation criteria, resulting in increased efficiency.