Full-scale　discharge　tests　of　transmission　line-tower　air　gaps　are　costly　and　time-consuming,　and　they　cannot　exhaustively　simulate　all　the　gap　configurations　in　practical　engineering.　In　this　paper,　a　machine　learning　model　established　by　support　vector　machine　is　introduced　to　predict　the　switching　impulse　discharge　voltages　of　the　ultra-high-voltage　(UHV)　AC　transmission　line-tower　air　gaps.　The　three-dimensional　finite　element　models　of　a　UHV　cup-type　tower　and　a　UHV　compact　transmission　line　were　established　for　electric　field　calculation,　and　some　features　were　extracted　from　the　hypothetic　discharge　channel　and　the　shortest　path　between　the　bundled　conductor　and　the　tower.　These　features　under　a　given　voltage　were　normalised　and　input　to　the　SVM　model,　while　the　output　is　two　binary　values,　respectively,　representing　gap　withstanding　or　breakdown.　Trained　by　experimental　data　of　one　type　of　the　UHV　transmission　line-tower　gaps,　the　SVM　model　is　able　to　predict　the　discharge　voltages　of　another　gap　type.　The　mean　absolute　percentage　errors　of　the　two　engineering　gap　types,　under　different　gap　distances,　are　8.31　and　4.86%,　respectively,　which　are　acceptable　for　engineering　applications.　The　results　provide　a　possible　way　to　obtain　the　discharge　voltages　of　complicated　engineering　gaps　by　mathematical　calculations.　©　2019　Institution　of　Engineering　and　Technology.All　right　reserved.