Timely detection of eye-related diseases is critical for preserving vision and preventing permanent visual loss. With the growing availability of ophthalmic imaging, artificial intelligence has emerged as an effective tool for enabling fast and automated disease screening. This study proposes a real-time artificial intelligence–driven framework for eye disease detection based on deep learning techniques. The system employs convolutional neural networks (CNNs) to process retinal fundus images and optical coherence tomography (OCT) scans for identifying prevalent eye conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration. To support real-time operation, the model architecture is optimized for low computational complexity and rapid inference without compromising diagnostic accuracy. The proposed system assists ophthalmologists by providing instant diagnostic feedback, reducing manual examination time, and supporting early clinical decision-making. Experimental evaluation demonstrates that the model achieves high detection accuracy along with minimal processing delay, making it suitable for real-time deployment in clinical settings, telemedicine platforms, and large-scale eye screening programs. The results highlight the potential of AI-based solutions to enhance accessibility, efficiency, and reliability in modern ophthalmic diagnosis.