Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, primarily affecting individuals with long-term diabetes. Early detection and timely treatment are crucial to preserving vision. Traditional diagnostic methods depend heavily on expert ophthalmologists, leading to subjective interpretations, limited accessibility, and delayed diagnosis. This theoretical research explores the integration of advanced image processing algorithms—encompassing machine learning, deep learning, and computer vision techniques—to enhance the screening and detection process of DR. Emphasis is placed on automated feature extraction, image enhancement, lesion segmentation, and classification using convolutional neural networks (CNNs) and hybrid AI models. The study aims to conceptualize a framework capable of improving accuracy, efficiency, and scalability in diabetic retinopathy screening.