Traditional　Medicine　(TM)　has　played　a　crucial　role　in　global　healthcare　due　to　its　long　history　and　holistic　approach.　Artificial　Intelligence　(AI)　has　emerged　as　a　revolutionary　technology,　offering　exceptional　capabilities　in　areas　such　as　data　mining,　pattern　recognition,　and　decision-making.　The　integration　of　Artificial　Intelligence　for　Traditional　Medicine　(AITM)　presents　a　promising　frontier　in　advancing　medicine　and　healthcare.　In　this　review,　we　explore　AITM　from　two　perspectives:　recent　AI　techniques　and　TM　applications.　Specifically,　we　investigate　how　Machine　Learning,　Deep　Learning,　and　Large　Language　Models　are　applied　to　TM,　covering　applications　such　as　diagnosis　(before,　during,　after)　and　research　(drug　research,　structured　knowledge,　data　analysis).　By　leveraging　advanced　algorithms　and　models,　AI　can　improve　decision-making　efficiency,　optimize　diagnosis　accuracy,　enhance　patient　experience,　and　reduce　costs.　We　anticipate　this　review　can　bridge　the　gap　between　AI　and　TM　communities.　And　the　goal　is　to　foster　collaboration　and　innovation　between　both　communities,　enabling　them　to　exploit　the　state-of-the-art　AI　techniques　to　advance　TM　diagnosis　and　research,　ultimately　contributing　to　the　enhancement　of　human　health.　©　2025　Center　for　Food　and　Biomolecules,　National　Taiwan　University