The　Monte　Carlo　simulation　method　for　turbomachinery　uncertainty　analysis　often　requires　performing　a　huge　number　of　simulations,　the　computational　cost　of　which　can　be　greatly　alleviated　with　the　help　of　metamodeling　techniques.　An　intensive　comparative　study　was　performed　on　the　approximation　performance　of　three　prospective　artificial　intelligence　metamodels,　that　is,　artificial　neural　network,　radial　basis　function,　and　support　vector　regression.　The　genetic　algorithm　was　used　to　optimize　the　predetermined　parameters　of　each　metamodel　for　the　sake　of　a　fair　comparison.　Through　testing　on　10　nonlinear　functions　with　different　problem　scales　and　sample　sizes,　the　genetic　algorithm-support　vector　regression　metamodel　was　found　more　accurate　and　robust　than　the　other　two　counterparts.　Accordingly,　the　genetic　algorithm-support　vector　regression　metamodel　was　selected　and　combined　with　the　Monte　Carlo　simulation　method　for　the　uncertainty　analysis　of　a　wind　turbine　airfoil　under　two　types　of　surface　roughness　uncertainties.　The　results　show　that　the　genetic　algorithm-support　vector　regression　metamodel　can　capture　well　the　uncertainty　propagation　from　the　surface　roughness　to　the　airfoil　aerodynamic　performance.　This　work　is　useful　to　the　application　of　metamodeling　techniques　in　the　robust　design　optimization　of　turbomachinery.