Purpose:　To　develop　a　machine　learning　model　to　predict　anatomical　response　to　anti-VEGF　therapy　in　patients　with　diabetic　macular　edema　(DME).　Methods:　This　retrospective　study　included　patients　with　DME　who　underwent　intravitreal　anti-VEGF　treatment　between　January　2023　and　February　2025.　Baseline　data　included　optical　coherence　tomography　(OCT)　features　and　blood-based　metabolic　and　hematologic　markers.　The　primary　outcome　was　defined　as　a　＞=　20%　reduction　in　central　retinal　thickness　(CRT)　post-treatment.　Feature　selection　was　performed　using　univariate　logistic　regression　and　LASSO　regression.　Five　machine　learning　algorithms-logistic　regression,　decision　tree,　multilayer　perceptron,　random　forest,　and　support　vector　machine-were　trained　and　validated.　Model　performance　was　evaluated　using　accuracy,　sensitivity,　specificity,　Area　Under　the　Receiver　Operating　Characteristic　Curve　(AUC),　and　decision　curve　analysis.　The　best-performing　model　was　further　interpreted　using　SHAP　analysis,　and　a　nomogram　was　constructed　for　clinical　application.　Results:　Among　the　37　baseline　variables,　five　key　predictors　were　identified:　preoperative　CRT　＞400　mu　m,　presence　of　retinal　edema,　presence　of　subretinal　fluid　(SRF),　disorganization　of　the　inner　retinal　layers　(DRIL),　and　ellipsoid　zone　(EZ)　integrity.　The　logistic　regression　model　achieved　the　best　performance　with　an　accuracy　of　0.83,　sensitivity　of　0.85,　specificity　of　0.79,　and　an　AUC　of　0.90　(95%　CI:　0.81-0.99).　SHAP　analysis　revealed　that　preoperative　retinal　edema,　DRIL,　SRF,　and　CRT　had　the　strongest　positive　contributions,　while　intact　EZ　was　a　negative　predictor　of　CRT　reduction.　A　nomogram　was　developed　to　facilitate　individualized　clinical　decision-making.　Conclusion:　We　successfully　developed　a　predictive　model　for　anatomical　response　to　anti-VEGF　therapy　in　DME　patients.　The　model　identified　key　features　associated　with　treatment　outcomes,　providing　a　valuable　tool　for　personalized　therapeutic　planning.　Further　validation　in　multicenter　cohorts　is　warranted　to　confirm　generalizability　and　enhance　model　robustness.