The　current　assessment　index　of　the　geological　hazard　vulnerability　assessment　for　mountain　road　network　is　relatively　simple,　and　the　assessment　methods　used　are　subjective,　complex,　and　inefficient.　This　study　proposes　a　prediction　model　for　geological　hazard　vulnerability　assessment　of　mountain　road　network　incorporating　machine　learning　algorithms.　First,　based　on　the　quantification　of　the　characteristics　of　the　mountain　road　network　and　the　local　rescue　forces,　an　objective　and　reasonable　index-based　system　of　vulnerability　assessment　of　the　mountain　road　network　was　constructed　by　combining　the　population,　economic,　and　material　factors.　Second,　the　FAHP　and　AHP-TOPSIS　were　applied　for　the　development　of　the　vulnerability　assessment　models　to　carry　out　the　preliminary　vulnerability　assessment　for　different　road　types.　Third,　the　results　of　the　preliminary　vulnerability　assessment　were　used　as　the　sample　set　to　build　a　road　vulnerability　prediction　model　using　SVM,　RF,　and　BPNN　algorithms.　Finally,　the　five-fold　cross-validation　and　statistical　parameter　accuracy　analysis　were　conducted　to　determine　the　most　reasonable　model　with　the　highest　prediction　accuracy　for　geological　hazard　vulnerability　mapping　of　the　mountain　road　network.　The　results　indicated　that　the　vulnerability　prediction　model　based　on　the　FAHP　sample　set　using　the　RF　algorithm　demonstrated　the　highest　accuracy　and　robustness.