The impact of lifestyle changes is alarming with the rise in different health issues among individuals irrespective of their age. Diabetics is one such health issue which has affected more than 400 million individuals of the world’s population. Untreated diabetes leads to different types of eye problems. This research focuses on employing Convolutional Neural Networks (CNNs) for the automated diagnosis of eye diseases. The objective is to improve diagnostic accuracy and interpretability by incorporating Explainable AI methods, specifically Local Interpretable Model-Agnostic Explanations (LIME). The research includes developing CNN models to classify retinal images into various eye diseases (diabetic retinopathy, glaucoma, cataracts, etc.) using a dataset and performance evaluation of these trained models. The research overcame this limitation by utilizing explainable AI techniques to provide visual justification of model predictions, leading to overall improved transparency and acceptance of AI-assisted diagnostics among clinicians. The reliable and effective approach in early detection and management is proved by rigorous experimentation and validation. In summary, the study helps bridge the divide between state-of-the-art AI technology and clinical work by providing clinicians with effective functionality that can help them to improve outcomes in ophthalmology.