In　order　to　improve　the　performance,　reduce　the　emissions　and　enhance　the　calibration　efficiency　of　a　gasoline　Wankel　rotary　engine　(WRE),　three　advanced　machine　learning　(ML)　methods,　including　artificial　neural　network　(ANN),　support　vector　machine　(SVM),　and　Gaussian　process　regression　(GPR),　were　applied　to　develop　the　prediction　model　of　the　torque,　fuel　flow,　nitrogen　oxide,　carbon　monoxide,　and　hydrocarbon.　The　effect　of　feature　numbers　was　examined　using　the　recommended　parameters　of　the　ANN,　SVM,　and　GPR　models.　It　was　concluded　that　using　speed,　manifold　absolute　pressure,　and　air　fuel　ratio　as　input　parameters　to　build　the　prediction　model　performed　best.　The　generalization　ability　of　the　three　ML　models　was　compared　on　the　interpolative　and　extrapolative　data　sets　using　the　extended　recommendation　parameters.　The　results　showed　that　the　GPR　model　performed　the　best　generalization　ability　in　scarce　data　sets　and　was　simpler　to　train　compared　with　ANN　and　SVM.　The　response　surfaces　constructed　using　the　GPR　model　were　very　smooth　and　accurate,　in　which　the　coefficient　of　determination　for　all　the　predicted　parameters　was　more　than　0.99.　It　is　strongly　proposed　that　the　GPR　approach　is　a　universal　approach　which　will　be　an　essential　direction　for　WRE　system　control　and　surrogate　model　modeling.