BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth factor (anti-VEGF) therapy in DME patients. Current diagnosis relies on optical coherence tomography (OCT) imaging, but manual interpretation is limited. This study aims to integrate 3D-OCT features and clinical variables to develop machine learning (ML) models for predicting anti-VEGF treatment outcomes. METHODS AND ANALYSIS: Medical records and 3D-OCT images of DME patients were included in this study. The 3D-OCT images were categorized into good and poor visual response groups based on the best corrected visual acuity at one month after three consecutive anti-VEGF treatments. The images and clinical features were subjected to assessment by 11 automatic classification models for anti-VEGF treatment responses in DME patients. The top 3 performing models were selected to build an ensemble model, and evaluated in the test dataset. RESULTS: This study included 142 patients with 3D-OCT images of 170 eyes. A total of 20 image and clinical features were selected for the model construction and test in DME patients responded to anti-VEGF therapy. Adaptive boosting (AdaBoost), GradientBoosting, and light gradient boosting machine (LightGBM) exhibited better performances than the remaining 8 models. The ensemble model constructed achieved a sensitivity of 0.941, specificity of 0.882, and accuracy of 0.912 in the test dataset, with an area under the receiver operating characteristic curve of 0.976. CONCLUSION: This study established an ensemble ML algorithm based on 3D-OCT images and clinical features for automatic detection of treatment responses to anti-VEGF treatment in DME patients to predict the efficacy of anti-VEGF treatment in DME patients and assist clinicians in optimal treatment decisions.