Diagnostic　pathology,　historically　dependent　on　visual　scrutiny　by　experts,　is　essential　for　disease　detection.　Advances　in　digital　pathology　and　developments　in　computer　vision　technology　have　led　to　the　application　of　artificial　intelligence　(AI)　in　this　field.　Despite　these　advancements,　the　variability　in　pathologists’　subjective　interpretations　of　diagnostic　criteria　can　lead　to　inconsistent　outcomes.　To　meet　the　need　for　precision　in　cancer　therapies,　there　is　an　increasing　demand　for　accurate　pathological　diagnoses.　Consequently,　traditional　diagnostic　pathology　is　evolving　towards　“next-generation　diagnostic　pathology”,　prioritizing　on　the　development　of　a　multi-dimensional,　intelligent　diagnostic　approach.　Using　nonlinear　optical　effects　arising　from　the　interaction　of　light　with　biological　tissues,　multiphoton　microscopy　(MPM)　enables　high-resolution　label-free　imaging　of　multiple　intrinsic　components　across　various　human　pathological　tissues.　AI-empowered　MPM　further　improves　the　accuracy　and　efficiency　of　diagnosis,　holding　promise　for　providing　auxiliary　pathology　diagnostic　methods　based　on　multiphoton　diagnostic　criteria.　In　this　review,　we　systematically　outline　the　applications　of　MPM　in　pathological　diagnosis　across　various　human　diseases,　and　summarize　common　multiphoton　diagnostic　features.　Moreover,　we　examine　the　significant　role　of　AI　in　enhancing　multiphoton　pathological　diagnosis,　including　aspects　such　as　image　preprocessing,　refined　differential　diagnosis,　and　the　prognostication　of　outcomes.　We　also　discuss　the　challenges　and　perspectives　faced　by　the　integration　of　MPM　and　AI,　encompassing　equipment,　datasets,　analytical　models,　and　integration　into　the　existing　clinical　pathways.　Finally,　the　review　explores　the　synergy　between　AI　and　label-free　MPM　to　forge　novel　diagnostic　frameworks,　aiming　to　accelerate　the　adoption　and　implementation　of　intelligent　multiphoton　pathology　systems　in　clinical　settings.