Many technologies are employed in sensor network research today to improve on past studies that prioritized time- and money-saving methods in addition to creative and innovative ideas. Typically, clustering is utilized to control wireless sensor network (WSN) energy considerations. Our primary focus in this study was on multi hop routing in a clustering setting. Our work categorized into two approach categories optimization-based and methodology-based based on cluster-related characteristics and attributes. Several techniques were found throughout the category, and the idea, limitations, benefits, and drawbacks are explained. As we know that there are various optimization algorithms that are Red Fox optimization, Blue Monkey optimization algorithms, etc. In this research, we introduce the unique parameters-considered CH Selection technique based on the Naïve Bayes Optimization. Based on this effort, we offer the audience relevant data that they can utilize to explore their research ideas and create a new model that addresses the shortcomings of WSN-based clustering methods. We have therefore created an energy-efficient Naïve Bayes-based clustering technique for WSN called EENBCP. Numerous simulations have shown that the recommended strategy extends the network's lifespan and conserves energy more effectively.