In　arid　and　semi-arid　lands　like　Iran　water　is　scarce,　and　not　all　the　wastewater　can　be　treated.　Hence,　groundwater　remains　the　primary　and　the　principal　source　of　water　supply　for　human　consumption.　Therefore,　this　study　attempted　to　spatially　assess　the　groundwater　potential　in　an　aquifer　in　a　semi-arid　region　of　Iran　using　geographic　information　systems　(GIS)-based　statistical　modeling.　To　this　end,　75　agricultural　wells　across　the　Marvdasht　Plain　were　sampled,　and　the　water　samples＇　electrical　conductivity　(EC)　was　measured.　To　model　the　groundwater　quality,　multiple　linear　regression　(MLR)　and　principal　component　regression　(PCR)　coupled　with　elven　environmental　parameters　(soil-topographical　parameters)　were　employed.　The　results　showed　that　that　soil　EC　(SEC)　with　Beta　=　0.78　was　selected　as　the　most　influential　factor　affecting　groundwater　EC　(GEC).　CaCO3　of　soil　samples　and　length-steepness　(LS　factor)　were　the　second　and　third　effective　parameters.　SEC　with　r　=　0.89　and　CaCO3　with　r　=　0.79　and　LS　factor　with　r　=　0.69　were　also　characterized　for　PC1.　According　to　performance　criteria,　the　MLR　model　with　R-2　=　0.94,　root　mean　square　error　(RMSE)　=　450　mu　Scm(-1)　and　mean　error　(ME)　=　125　mu　Scm(-1)　provided　better　results　in　predicting　the　GEC.　The　GEC　map　indicated　that　16%　of　the　Marvdasht　groundwater　was　not　suitable　for　agriculture.　It　was　concluded　that　GIS,　combined　with　statistical　methods,　could　predict　groundwater　quality　in　the　semi-arid　regions.