Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. They are deployed remotely in sever environment which doesn’t have an infrastructure. Energy is a limited resource which needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for longtime. The majorobjectiveofthisresearchistoefficientlyconsumeenergybasedontheNeuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The significance of this study is to examine the challenges of energy efficient algorithms and the network lifetime on WSN. So that they can assist several applications. Clustering is one of the hierarchical based routing protocols which manage the communicationamongsensornodeandsinkviaClusterHead(CH),CHisresponsibletosendand receiveinformationfrommultiplesensornodesandmultiplebasestations(BS).Moreover theloadbalancebetweenthesensornodeandsinknodeisachievedthroughit. Thereare variousalgorithmsthatcanefficientlyselectappropriateCHandlocalizethemembership ofClusterwithfuzzylogicclassificationparameterstominimizeperiodicclusteringwhich consumes more energy and we have applied neural network learning algorithm to learn variouspatternsbasedonthefuzzyrules. Wehavemeasuredhowmuchenergyhassaved from different algorithms and we have compared to our Neuro-fuzzy logic. ConsequentlywehavedemonstratedthatourNeuro-Fuzzymodeloutperformsmorethan the other algorithms namely energy aware unequal cluster fuzzy (EAUCF), energy aware cluster Neuro-Fuzzy (EACNF) and energy efficient cluster formation (EECF).