The retina is a critical component of the eye responsible for capturing visual information, making the importance of retinal health for clear vision. Various eye diseases, such as age-related macular degeneration, diabetic retinopathy, and glaucoma, can severely impair vision and even lead to blindness if not detected and treated early. Therefore, automated systems using machine learning and computer vision techniques have shown promise in the early detection and management of these diseases, reducing the risk of vision loss. In this context, to facilitate the development and evaluation of machine learning models for eye disease detection, we introduced a comprehensive dataset which was collected during a span of eight months from Anawara Hamida Eye Hospital & B.N.S.B. Zahurul Haque Eye Hospital using Color Fundus Photography machine. The dataset has two categories of data: color fundus photographs and anterior segment images. The color fundus photographs categorized into nine classes: Diabetic Retinopathy, Glaucoma, Macular Scar, Optic Disc Edema, Central Serous Chorioretinopathy (CSCR), Retinal Detachment, Retinitis Pigmentosa, Myopia, Healthy and anterior segment images has one class: Pterygium. This dataset comprises 5335 primary images. By providing a rich and diverse collection of color fundus photographs, this dataset serves as a valuable resource for researchers and clinicians in the field of ophthalmology for the automatic detection of nine different classes of eye diseases.