If overlooked diabetic retinopathy (DR), a degenerative eye condition that affects people with diabetes, can cause serious vision loss. Early DR detection is essential for prompt management and action. Deep convolutional neural networks (DCNNs) have become effective tools for automated processing of retinal pictures in recent years, making it possible to diagnose DR earlier. To learn and extract distinguishing features from retinal fundus images, the suggested method makes use of a DCNN architecture. For the training of models and evaluation, a dataset containing quite a few of retinal pictures from diabetic patients—including both normal and DR cases is utilised. Combining methods of supervised learning, the DCNN model undergoes training with a focus on improving performance indicators including accuracy, sensitivity, and specificity. To intentionally enhance the quantity and diversity of the training dataset, methods like image rotation, flipping, scaling and random cropping are used. This successfully simulates changes in the circumstances of collecting images and the clinical traits of DR. The efficiency of the suggested DCNN-based technique for early DR detection is shown by experimental findings. The trained model performs exceptionally well in spotting early indications of DR, such as microaneurysms, haemorrhages, and exudates, with high accuracy. An independent test dataset is used to assess the model’s performance, highlighting its potential for use in practical situations. The suggested approach advances automated DR detection, enabling immediate action and greater control of this illness that threatens eyesight. The suggested DCNN-based approach needs to be further improved and validated, and the resulting system needs to be integrated into the current medical system to facilitate widespread early DR detection and treatment. The model achieved validation accuracy of 76.80% and training accuracy of 99.58%. A training loss of 0.0132 and a validation loss of 1.9230 have also been attained.