In order to make a mineral exploration program successful, a large amount of exploratory data including geophysical, geochemical and remotely sensed images gathered during different mineral exploration stages are needed. It is well known that all exploratory data are inherently associated with some sort of uncertainties so employing data driven methods such as Boolean or weight of evidence, may cause significant errors in the course on data integration. Therefore, most researchers have focused on using knowledge driven methods such as fuzzy logic and more recently the combined fuzzy and neural network approaches called neuro-fuzzy method which deploy the privileges of both data and expert knowledge simultaneously. The investigated area is bounded by the Varcheh geological map which is part of Sanandaj-Sirjan structural zone. This zone is considered as highly favorable for Pb/Zn MVT mineralization. In this study the available exploratory data including geochemical stream samples, airborne magnetic data, alteration zones including silicified, dolomitized and iron oxide minerals derived from remotely sensed imagery and other lithological and structural layers derived from 1:100000 geological map were preprocessed and prepared for mineral potential mapping. Through employing all prepared exploratory evidences, the favorability data integration methodology based on using both fuzzy logic and neuro-fuzzy approaches were carried out. The results showed that the fuzzy logic approach alone could detect 74 percent of all 39 known Pb/Zn mineralization while the neuro-fuzzy approach outperformed the fuzzy logic approach by including 82 percent of all 39 known mineralization prospects inside the high priority zones of final favorability map