Retinal diseases can have severe consequences if left undetected and untreated. Early and accurate diagnosis is crucial for effective treatment and prevention of vision loss. In recent years, convolutional neural networks (CNNs) have emerged as a powerful tool for automated image classification, making them a promising approach for early detection of retinal abnormalities. This review paper provides a comprehensive overview of the state-of-the-art in CNN-based retinal image classification for the early detection of various eye diseases. We examine the key advancements in CNN architectures, training techniques, and dataset curation that have driven significant improvements in classification accuracy. Additionally, we discuss the importance of this technology for enhancing clinical decision-making, improving patient outcomes, and reducing the burden on healthcare systems. Through a comparative analysis of recent studies, we identify areas for further research and highlight the potential for continued refinement and real-world deployment of these intelligent systems. Overall, this review underscores the transformative impact of CNN-based retinal image analysis in revolutionizing early diagnosis and management of retinal conditions.