This paper presents a deep learning-based approach to classify eye diseases using Convolutional Neural Networks (CNN) on Optical Coherence Tomography (OCT) images. The proposed method aims to detect three specific eye diseases—Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD)—from retinal images. The CNN model, trained on a publicly available dataset, includes convolutional, ReLU, pooling, flatten, and softmax layers. Image processing techniques, such as brightness and contrast adjustment, and the Multiscale Retinex Algorithm (MRA), are applied to enhance the images before feeding them into the CNN. The model achieves high accuracy, with a classification accuracy of 97% after fine-tuning. The paper also compares the proposed model with other recent studies, demonstrating its superior performance in detecting multiple eye diseases. The results highlight the potential of deep learning in early disease detection, which is crucial for better treatment outcomes and preventing permanent vision loss.This paper presents a deep learning-based approach to classify eye diseases using Convolutional Neural Networks (CNN) on Optical Coherence Tomography (OCT) images. The proposed method aims to detect three specific eye diseases—Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD)—from retinal images. The CNN model, trained on a publicly available dataset, includes convolutional, ReLU, pooling, flatten, and softmax layers. Image processing techniques, such as brightness and contrast adjustment, and the Multiscale Retinex Algorithm (MRA), are applied to enhance the images before feeding them into the CNN. The model achieves high accuracy, with a classification accuracy of 97% after fine-tuning. The paper also compares the proposed model with other recent studies, demonstrating its superior performance in detecting multiple eye diseases. The results highlight the potential of deep learning in early disease detection, which is crucial for better treatment outcomes and preventing permanent vision loss.