Diabetic retinopathy is one of the common causes that can bring about vision impairment; therefore, early diagnosis with accuracy is of paramount importance. This paper presents a novel hybrid model of deep transfer learning and feature fusion for diabetic retinopathy classification and grading in terms of severity. In the proposed architecture, pretrained convolutional neural networks are used to adapt effective features, which in turn are fused with robust techniques to further enhance the overall classification performance. Extensive experiments have been conducted using publicly available datasets of DRs for performance comparison with traditional methods. Hybrid models achieve very high improvements both in classification and grading accuracy, thus showing their potential toward real-world clinical applications. It can be shown from our experimental results that this deep transfer learning, combined with feature fusion, is a fairly robust solution toward automated diagnosis of DR. This work opens up future prospects for further advancements in medical image analysis, ultimately helping to improve patient outcomes by timely and accurate DR detection.