Ophthalmic disorders such as diabetic retinopathy, agerelated macular degeneration, and glaucoma are primary preventable causes of blindness. Early and precise diagnosis is critical to ensuring early therapeutic intervention and mitigating long-term ophthalmic and systemic complications. In this study, we propose a novel deep learning based hybrid framework, RetinaXNet, that integrates DenseNet201 with MultiHead Self-Attention (MHSA) to enhance diagnostic performance and model interpretability in fundus image analysis. The hybrid framework integrates tailored preprocessing techniques to optimize retinal image quality and leverages explainable AI (XAI) through Gradient-weighted Class Activation Mapping (Grad-CAM). This is particularly to provide clinically relevant visualizations of disease-localized regions. Unlike prior models that have limitations in generalizability, poor interpretability, or excessive computational overhead, the proposed approach achieves a balanced trade-off between predictive accuracy, transparency, and efficiency. Experimental evaluation on benchmark datasets demonstrates a test accuracy of 99.26%, a cross-validation accuracy of 99.05%, an F1-score of 99.26%, and a Cohen’s Kappa coefficient of 0.9902. The performance highlights not only high predictive fidelity but also strong inter-rater agreement. Grad-CAM visual outputs align consistently with ophthalmologist-marked pathological regions for reinforcing the model’s potential for clinical adoption. Moreover, the lightweight design supports real-time deployment via a web-based interface, making it suitable for use in low-resource or point-of-care settings.