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Soft computing tools play a vital role in fixing certain non-linear problems related to the earth. More specifically, digging out the mysteries of subsurface of the earth, the nonlinearity can be converging to assemble an approximate solution which resembles the real characteristics of the earth. Adaptive Neuro Fuzzy Inference System (ANFIS) tool is one of the best soft computing tools to estimate the complex data analysis. ANFIS was applied to estimate the subsurface parameters of earth using the Vertical Electrical Sounding (VES) data. Classifying the lithology based on the resistivity values by ANFIS is employed here in this paper. As the resistivity of each formation varies in range of values, ANFIS tool thus approximates the subsurface features based on effective training. In this study, ANFIS performance was checked with training data, and successively it has been tested with the field data. Optimized ANFIS algorithm provides the necessary tool for predicting the non-linear subsurface features. The best training performance of this soft computing tool efficiently predicts the subsurface lithology. Also the interpreted results show the true resistivity and thickness of the subsurface layers of the earth. The proposed technique was represented in Graphical User Interface (GUI), and the lithological variables are predicted in texture format and linguistic variables.