Glaucoma is a leading cause of permanent blindness globally, often progressing asymptomatically until it reaches advanced stages. Regular monitoring and early detection are vital to avoid vision loss, making efficient diagnostic methods crucial. While traditional diagnostic techniques are effective, they often can be time-consuming and require specialized expertise. There is a growing need for scalable, accurate, and automated methods to diagnose glaucoma early and efficiently, especially in resource-limited settings. This paper explores various machine learning (ML) techniques to address this need, including decision trees, ensemble methods, logistic regression models, neural networks, Support Vector Machines (SVM) with different kernels, etc. These models were trained and evaluated on a dataset to identify the most accurate and efficient approach for diagnosing glaucoma. The feature importance was also assessed using MRMR and Chi-Square algorithms, revealing RNFL4_mean as the most critical feature for diagnosis. The methods employed demonstrated varied levels of accuracy, with ensemble methods using boosted trees and bagged trees models achieving the highest accuracy both at 93.19%. This high level of accuracy highlights the potential of leveraging advanced ML techniques to enhance the early detection and diagnosis of glaucoma.