Landslide　susceptibility　mapping　of　mountain　roads　is　frequently　confronted　by　insufficient　historical　landslide　sample　data,　multicollinearity　of　existing　evaluation　index　factors,　and　inconsistency　of　evaluation　factors　due　to　regional　environmental　variations.　Then,　a　single　machine　learning　model　can　easily　become　overfitting,　thus　reducing　the　accuracy　and　robustness　of　the　evaluation　model.　This　paper　proposes　a　combined　machine-learning　model　to　address　the　issues.　The　landslide　susceptibility　in　mountain　roads　were　mapped　by　using　factor　analysis　to　normalize　and　reduce　the　dimensionality　of　the　initial　condition　factor　and　generating　six　new　combination　factors　as　evaluation　indexes.　The　mountain　roads　in　the　Youxi　County,　Fujian　Province,　China　were　used　for　the　landslide　susceptibility　mapping.　Three　most　frequently　used　machine　learning　techniques,　support　vector　machine　(SVM),　random　forest　(RF),　and　artificial　neural　network　(ANN)　models,　were　used　to　model　the　landslide　susceptibility　of　the　study　area　and　validate　the　accuracy　of　this　evaluation　index　system.　The　global　minimum　variance　portfolio　was　utilized　to　construct　a　machine　learning　combined　model.　5-fold　cross-validation,　statistical　indexes,　and　AUC　(Area　Under　Curve)　values　were　implemented　to　evaluate　the　predictive　accuracy　of　the　landslide　susceptibility　model.　The　mean　AUC　values　for　the　SVM,　RF,　and　ANN　models　in　the　training　stage　were　89.2%,　88.5%,　and　87.9%,　respectively,　and　78.0%,　73.7%,　and　76.7%,　respectively,　in　the　validating　stage.　In　the　training　and　validation　stages,　the　mean　AUC　values　of　the　combined　model　were　92.4%　and　87.1%,　respectively.　The　combined　model　provides　greater　prediction　accuracy　and　model　robustness　than　one　single　model.