Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection through regular screening of retinal fundus images. Leveraging advancements in Deep Learning and the Internet of Things (IoT), this study proposes a novel automated diagnostic system. Deep Learning models, specifically Convolutional Neural Networks (CNNs), are trained on a large dataset of retinal images to classify them based on disease severity. IoT facilitates real-time image acquisition and transmission, enhancing accessibility and efficiency in remote or underserved areas. Key stages include preprocessing, feature extraction, and classification using state-of-the-art neural networks such as ResNet and EfficientNet. Evaluation metrics such as sensitivity, specificity, and accuracy validate the system's performance against clinical standards. Integration of IoT enables seamless data flow from image capture to diagnosis, optimizing healthcare delivery. This approach holds promise for scalable and cost-effective DR screening, potentially transforming diabetic eye care globally.