This paper to prevent irreversible vision impairment, it is critical to identify retinal diseases as soon as possible. At this time, most machine learning methods have been designed to identify a single retinal disease - diabetic retinopathy, glaucoma or age-related macular degeneration (AMD). Therefore, these systems are not optimal for complete disease screening of multiple retinal disorders. To address this challenge, our work proposes a supervised hybrid deep learning model that combines Convolutional Neural Networks with Vision Transformers, creating four-class fundus image classification - Normal, Diabetic Retinopathy, Glaucoma, and Age-Related Macular Degeneration. The CNN creates a localized representation of lesion characteristics such as micro aneurysms, hems, exudates, optic cup morphology, and drusen, while the VIT produces a higher-level global classification of characteristics such as disc shape, vessel patterns, and macula texture. By combining both types of characterization, our hybrid system resolves the limitations of the traditional single-disease and CNN-only networks, and will provide better performance through improved accuracy, sensitivity, and specificity as a fast, automated, and dependable method for multi-disease screening of retinal disease in clinical practice.