Meibomian　gland　dysfunction　(MGD)　is　increasingly　recognized　as　a　critical　contributor　to　evaporative　dry　eye,　significantly　impacting　visual　quality.　With　a　global　prevalence　estimated　at　35.8　%,　it　presents　substantial　challenges　for　clinicians.　Conventional　manual　evaluation　techniques　for　MGD　face　limitations　characterized　by　inefficiencies,　high　subjectivity,　limited　big　data　processing　capabilities,　and　a　dearth　of　quantitative　analytical　tools.　With　rapidly　advancing　artificial　intelligence　(AI)　techniques　revolutionizing　ophthalmology,　studies　are　now　leveraging　sophisticated　AI　methodologies-including　computer　vision,　unsupervised　learning,　and　supervised　learning-to　facilitate　comprehensive　analyses　of　meibomian　gland　(MG)　evaluations.　These　evaluations　employ　various　techniques,　including　slit　lamp　examination,　infrared　imaging,　confocal　microscopy,　and　optical　coherence　tomography.　This　paradigm　shift　promises　enhanced　accuracy　and　consistency　in　disease　evaluation　and　severity　classification.　While　AI　has　achieved　preliminary　strides　in　meibomian　gland　evaluation,　ongoing　advancements　in　system　development　and　clinical　validation　are　imperative.　We　review　the　evolution　of　MG　evaluation,　juxtapose　AI-driven　methods　with　traditional　approaches,　elucidate　the　specific　roles　of　diverse　AI　technologies,　and　explore　their　practical　applications　using　various　evaluation　techniques.　Moreover,　we　delve　into　critical　considerations　for　the　clinical　deployment　of　AI　technologies　and　envisages　future　prospects,　providing　novel　insights　into　MG　evaluation　and　fostering　technological　and　clinical　progress　in　this　arena.