Due to presence of inherent uncertainties in geosciences data caused by various unknown and even known geological phenomenon, applying simple Boolean logics to infer from such data would eventually lead to significant estimation errors. One way out of this difficulty is to employ knowledge based methods such as Fuzzy logic inference models which handles such uncertainties through considering gradual nature of properties of qualitative parameters under investigation. Fuzzy logic models are considered as knowledge based techniques and when they exploit the advantages of data driven techniques such as neural network form a very powerful justify; LINE-HEIGHT: normal; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" Kerman metalogenic belt, makes south part of Orumiye-Dokhtar metalogeny province and is known as the richest copper containing belt of Iran . More than 200 deposites and mineral indexes are known in this belt which most of them are porphyries. The geology, remote sensing and airborn geophysics of the area has been studied. In geology studies , layers separating have been done. In remote sensing studies, frequency of Iron oxides and hydroxide ions beside lineations have been studied. In term of geophysics, reduce to pole correction has been done on airborn magnetic data, being achieved analytical signal. The results were 6 data layers. After that, gathering information has been done by fuzzy method and neural network. In all methods, 100 training points has been used and then modeling has been done. the results showed that 88.9% of known deposits and mineral indexes in area with suitable potential are known by neural network and 80.2% of deposits and mineral indexes are known by fuzzy method. In additional both of them are similar by comparing various methods can be concluded that neural network because of its compatibility with geological structures are more coincided with truth and are more useful potential mapping in GIS.