This　multicenter　retrospective　study　developed　and　validated　an　AI　driven　model　for　automated　segmentation　and　quantitative　evaluation　of　meibomian　glands　using　infrared　meibography　images　acquired　by　the　Keratograph　5M　device.　A　total　of　1350　infrared　meibography　images　were　collected　and　annotated　for　model　training　and　validation.　The　model　demonstrated　high　segmentation　performance,　with　an　Intersection　over　Union　of　81.67%　(95%　Confidence　Interval　[CI]:　81.03-82.31)　and　accuracy　of　97.49%　(95%　CI:　97.38-97.62),　outperforming　conventional　algorithms.　The　agreement　was　observed　between　AI-based　and　manual　gland　grading　(Kappa　=　0.93)　and　gland　counting　(Spearman　r　=　0.9334).　Repeatability　analysis　confirmed　the　model＇s　stability,　and　external　validation　across　four　independent　centers　yielded　consistent　results　with　AUCs　exceeding　0.99.　This　AI　tool　offers　a　standardized,　efficient,　and　objective　method　for　meibography　image　analysis,　which　may　improve　diagnostic　precision　and　assist　in　the　clinical　management　of　meibomian　gland　dysfunction　across　diverse　populations.