In wireless sensor networks (WSNs), one of the key issues is energy efficiency, which can be effectively resolve with the help of Artificial Intelligence (AI)-driven models. The transmission of energy to sensor nodes is the most energy-intensive process, particularly when vast distances are involved. Clustered routing systems are efficient ways to lower transmission energy and increase network lifetime. This paper presents a clustered routing technique using an AI-driven self-organizing map (SOM) for optimizing energy in wireless sensor networks (WSNs). Also, this paper explores these challenges and proposes an AI-driven Self-Organizing Map (SOM) model for energy optimization in cluster-based WSNs. Using clustering-based algorithms with self-organizing maps (SOM), the AI-based approach reduces energy consumption in networks with constrained resources. The network can be made more energy efficient by clustering its nodes together so that data can be transferred amongst them without having to be sent over long distances. We optimized the energy consumption and communication overhead in WSN using the self-organizing map (SOM) clustering method. In this work, we have used Matlab R2013b. The performance analysis has been performed on 100, 200, and 300 nodes in the SOM topology.