This study focuses on automating the diagnosis of glaucoma, a leading cause of irreversible vision loss, through machine learning classifiers (MLCs). Using data from 605 patients in Ghana, the research integrated Optical Coherence Tomography (OCT) and Visual Field Test (VFT) parameters to predict glaucoma status. Ten machine learning algorithms were tested, with Naïve Bayes achieving the highest diagnostic accuracy (AUROC 0.92). The findings suggest that machine learning can significantly improve diagnostic efficiency and accuracy, particularly in resource-constrained settings.