The eye, an intricate organ, is the most important sensory input to our perception of the world. The retina plays an important part in this process and any impairment to this part of the eye leads to a degradation to the quality of human life .The timely and apt diagnosis of retinal diseases is imperative for preventing loss of vision and improving patient experience and post-diagnosis care. This work assembles a panoramic view of the evolution of Retinal Disease Classification and Detection methods ranging from traditional image processing techniques to advanced deep learning models such as Support Vector Machines(SVMs), Random Forest Classifiers, Convolutional Neural Networks(CNNs) like ResNet50 and Inception V3, Bayesian Classifiers, including cutting-edge technology like Vision Transformers with attention mechanisms and finally methodologies consisting hybrids between CNNs and Improved SVMs, ViTs, CNNs, etc. The emphasis is placed upon three commonly diagnosed diseases: diabetic retinopathy, glaucoma and age-related macular degeneration, the features of which are extracted from datasets in (fundus) Photography and Optical Coherence Tomography(OCT) images. With comparisons based on performance, strengths, and weaknesses of each milestone of algorithms, this work sheds light on current trends and key breakthroughs in Retinal Disease Classification and Detection methods. Offering appreciation for each algorithm as well as becoming a contributor to guiding future research for automated diagnosis of retinopathy. The comparative study clearly shows many milestones achieved throughout the duration of time, plain classifiers as well as extractors showed amazing results, but the hybrid models outperformed every other model by achieving 99% accuracy.