PURPOSE: To predict the anti-vascular endothelial growth factor (VEGF) therapeutic response of diabetic macular oedema (DME) patients from optical coherence tomography (OCT) at the initiation stage of treatment using a machine learning-based self-explainable system. METHODS: A total of 712 DME patients were included and classified into poor and good responder groups according to central macular thickness decrease after three consecutive injections. Machine learning models were constructed to make predictions based on related features extracted automatically using deep learning algorithms from OCT scans at baseline. Five-fold cross-validation was applied to optimize and evaluate the models. The model with the best performance was then compared with two ophthalmologists. Feature importance was further investigated, and a Wilcoxon rank-sum test was performed to assess the difference of a single feature between two groups. RESULTS: Of 712 patients, 294 were poor responders and 418 were good responders. The best performance for the prediction task was achieved by random forest (RF), with sensitivity, specificity and area under the receiver operating characteristic curve of 0.900, 0.851 and 0.923. Ophthalmologist 1 and ophthalmologist 2 reached sensitivity of 0.775 and 0.750, and specificity of 0.716 and 0.821, respectively. The sum of hyperreflective dots was found to be the most relevant feature for prediction. CONCLUSION: An RF classifier was constructed to predict the treatment response of anti-VEGF from OCT images of DME patients with high accuracy. The algorithm contributes to predicting treatment requirements in advance and provides an optimal individualized therapeutic regimen.