This paper used multivariate regression to create a mathematical model (with reasonable accuracy) for iron skarn exploration in the region of the interest and generalizing multivariate regression in Mineral Prospectivity Mapping (MPM) field. The main target of this manuscript is to exert multivariate regression analysis (as a MPM method) to iron outcrops mapping from northeast part of the study area to discover new iron deposits in other parts. Two types of multivariate regression models as two linear equations were employed to discover new mineral deposits. The Aster satellite image bands (14 bands) sets as Unique Independent Variables (UIVs) and iron outcrops map as dependent variables were used for MPM. According to the results of p-value, <i>R</i><sup>2</sup> and <i>R</i><sup>2</sup><sub><i>adj</i></sub>, the second regression model (which was a multiples and exponents of UIVs) was the fitted model versus other models. Also the accuracy of the model was confirmed by iron outcrops map and geological observations. Based on field observation iron mineralization occurs as contact of limestone and intrusive rocks (skarn type). Iron minerals consist dominantly of magnetite, hematite and goethite.