Glaucoma is a leading cause of irreversible blindness worldwide, frequently progressing without noticeable symptoms until significant vision loss occurs. Early detection is essential for managing and mitigating its effects. However, traditional diagnostic methods, such as intraocular pressure tests and optical coherence tomography (OCT), may not be easily accessible in resource-limited areas. Fundus imaging, which captures the back of the eye, provides a non-invasive method to assess the optic nerve and retinal health, making it an effective option for glaucoma screening. By leveraging large datasets of labeled fundus images, machine learning algorithms can be trained to recognize early structural changes in the optic nerve and surrounding retinal nerve fiber layers indicative of glaucoma. Convolutional Neural Networks (CNNs) are especially useful in this application, as they can automatically extract relevant features from complex images without requiring manual intervention.This paper explores a machine learning-based approach for glaucoma detection using fundus images, aiming to develop an accessible and efficient diagnostic tool. This paper proposed Sobeledge detection for data preprocessing, Convolutional Neural Networks (CNNs) for model selection, training, and evaluation. The proposed approach has the potential to provide accurate and scalable glaucoma screening solutions, potentially aiding early diagnosis and reducing the burden on healthcare systems. This method attains better accuracy rate.