Conditions affecting the retina, such as Diabetic Retinopathy (DR), Age-Related Macular Degeneration (ARMD), and glaucoma, are significant causes of irreversible vision impairment globally. The identification of these conditions quickly and precisely is invaluable. Unfortunately, noise, poor contrast, and complex anatomical features are challenges in retinal image analysis, particularly when detecting small diseases and small vessel margins. This work presents a new deep learning framework that combines a Residual Convolutional Neural Network (Residual CNN) and Long Short-Term Memory networks (DiaCNN-LSTM) to improve disease detection accuracy. During preprocessing, a Smart Adaptive Retinal Pre-processing (SARP) method is applied that integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) and bilateral filtering, followed by Canny edge detection to highlight structural details while sacrificing noise and artifacts. For feature extraction, multiple descriptors of Statistical Intensity Histogram (SIH), Weber Local Descriptor (WLD) enhanced with Discrete Cosine Transform (DCT), Local Optimal Oriented Pattern (LOOP) and Histogram of Oriented Gradients (HOG) are used. Data augmentation methods, such as rotation, flipping and cropping, improve the variability of the training samples. Clustering is performed using k Means through Empirical Bregman Divergence (EBD), and classification is done by a customized DiaCNN-LSTM architecture for diabetic retinal disease detection. This architecture allows for accurate classification in difficult cases, particularly early or ambiguous cases. On the RFMiD and APTOS2019 datasets, experimental results show that the proposed model outperforms state-of-the-art methods with an accuracy of 98.86% and Cohen’s Kappa value of 98.24 (%). The AI model that is being presented is scalable and interpretable, making it suitable for clinical use, especially for real-time monitoring by teleophthalmology and low-resource healthcare facilities.