Applying statistical methods in geochemical exploration is highly attractive due to their lower expenses and better results. In this research, univariate and multivariate statistical methods are used in order to process and interpret surface rock geochemical data of the Darreh-Zar porphyry copper deposit. Processing and analyzing surface data were done by using classic univariate statistical methods as well as multivariate methods such as principle component analysis (PCA), factor analysis (FA) and clustering methods by specifying effective elements associated with porphyry copper mineralization. Non-structural methods such as median absolute deviation (MAD) and standard deviation values, and structural methods including fractal geometry are used to determine thresholds of copper to identify further anomalies. Moreover, fractal method is also used to obtain threshold of copper deposit and further anomalies related to three variables of average assay, assay summation and productivity of copper which have been used in processing bore hole data. Support vector machine (SVM) and linear discriminant analysis (LDA) were used for geochemical modeling from the three mentioned variables and their relationship with sub-surface mineralization and surface data. The results of LDA for average assay variable are located in the central part of the ore deposit in a NW-SE direction. Anomalies obtained by quadratic discriminant analysis do not show any meaningful trends. Plotted distribution map of assay summation and productivity variables were similar to LDA results. Additionally, linear and non-linear SVM methods (with Gaussian kernel function) are used for geochemical modeling. Among these methods, 70 percent of data were used as training data while the remaining 30 percent were considered as the testing data. The results show that non-linear support vector machine method with Gaussian kernel function is more accurate than discriminant analysis.