Diabetic Retinopathy (DR) is a serious eye disease caused by long-term diabetes. It is one of the main causes of blindness around the globe. Early detection and prompt treatment are crucial to prevent permanent vision loss. Unfortunately, traditional diagnostic methods depend on the manual inspection of retinal fundus images by ophthalmologists. This process is time-consuming, subjective, and requires specialized skills. This project presents a Hybrid CNN-MLP Model for automated detection and classification of diabetic retinopathy using retinal images. The system combines Convolutional Neural Networks (CNN) for feature extraction and Multilayer Perceptron (MLP) for classification. The CNN component effectively captures spatial features like microaneurysms, hemorrhages, and exudates. Meanwhile, the MLP classifies these features into different levels of DR severity. The system is created using Python, TensorFlow/Keras, and Flask for online interaction. Users can upload retinal images, enter patient information, and receive real-time predictions with confidence scores, medical suggestions, and downloadable PDF reports. The system also keeps a record of patient history and provides visual analytics through graphs. This proposed model shows better accuracy, efficiency, and usability. It serves as a valuable tool for early screening and supports healthcare professionals in making decisions.