AC　surface　flashover　is　a　prevalent　insulation　fault　in　gas-insulated　equipment.　Given　the　complex　dependency　of　this　fault　on　various　factors　and　the　limitations　of　destructive　experimental　methods,　this　paper　proposes　the　use　of　tree-based　machine　learning　models　as　an　economical　and　efficient　alternative　to　obtain　the　flashover　voltage.　The　analysis　concentrates　on　salient　features　impacting　AC　surface　flashover,　such　as　electroluminescence　(EL),　water　contact　angle　(WCA),　and　percentage　of　silicon　atoms　(PSA).　The　extra　trees　algorithm　was　employed　and　optimized　by　feature　selection.　The　model,　utilizing　six　features—EL,　WCA,　PSA,　Polished　status,　Ra,　and　surface　resistivity—yielded　excellent　performance　with　MAE,　MSE,　MAPE,　and　R2　scores　of　0.50,　0.37,　1.47%,　and　0.93,　respectively.　The　study　highlights　the　potential　of　tree-based　models　in　providing　insights　and　prediction　of　surface　flashover　voltage　for　the　insulation　in　gas-insulated　equipment.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.