Eye diseases comprise one of the most challenging problems in the medical field because millions of people across the globe experience them. These conditions are always easier to manage if early detection and correct categorization are done. This paper presents a novel approach to eye disease classification using deep learning techniques, specifically leveraging transfer learning with a fine-tuned In EfficientB0-ResNet50 model. The proposed method focuses on classifying retinal images into four categories: The groups measured were cataract, glaucoma, diabetic retinopathy, and normal. The proposed model shows significant enhancements in terms of point accuracy, precision, average recall, and the overall model’s performance with the fine-tuning mechanisms applied. Significantly, our study presents a strong, automatic system for diagnosing eye diseases using EfficientNetB0 and ResNet50. These improvements make the model suitable for clinical use as they demonstrate improvements in the ability to distinguish between various ocular conditions. It is also beneficial to both early detection and treatment planning in that it assists clinicians in creating and implementing individualized management strategies, and saves time and resources. The current study puts a foundation for applying artificial intelligence to ophthalmology, where accuracy was raised from 90% before fine-tuning to 95% after fine-tuning. It opens up the prospects for enhancing the quality of treatment for patients and can create a basis for the reorganization of the treatment of eye diseases in clinics.