Retinal disorders, which cause vision impairment and blindness, offer a serious worldwide health concern. The advent of artificial intelligence (AI) techniques, together with advances in optical coherence tomography (OCT) imaging, has transformed the discipline of ophthalmology by allowing automated retinal disease classification. This chapter describes how to use AI-based algorithms to classify retinal diseases using OCT images. In this chapter, we suggest a deep learning–based strategy for the diagnosis of OCT images, namely the usage of convolutional neural networks and certain pretrained image classification models on top of convolutional neural networks. We also play around with the features derived from pretrained image classification models. This study focuses on three diseases: drusen, diabetic macular edema, and choroidal neovascularization. This chapter describes AI-based retinal disease classification approaches based on OCT images, emphasizing its potential to improve early identification, improve treatment outcomes, and reduce the burden on healthcare systems. The use of AI algorithms in retinal disease classification has various advantages, including automated analysis of massive datasets, early illness diagnosis, and resource optimization. As a result, AI-based retinal disease classification based on OCT images has the potential to change ophthalmology by boosting diagnostic accuracy and facilitating early intervention. In disease classification, machine learning and deep learning algorithms and transfer learning have shown promising outcomes. Addressing the hurdles and encouraging collaboration will pave the road for AI systems to be integrated into ordinary clinical practice, ultimately benefiting patients with retinal diseases.