Glaucoma as an important cause of irreversible blindness has a great impact on the quality of patients’ life, society, and the healthcare budget. Early detection of glaucoma can decrease its impact. Primary open-angle glaucoma is one of the most prevalent types of glaucoma. This study aims to evaluate five machine learning methods (LDA, LR, KNN, RF, and SVM) to find the method with the highest accuracy. 258 antigens were analyzed in 30 patients with exfoliative glaucoma and 30 healthy donors. By performing principal component analysis and heatmap clustering, two outlier samples were detected. A moderated t-statistics test was conducted by using the limma package from R to identify the differential reactivity of antibodies binding to antigens. Differential antigens were sorted based on the p-value from low to high. Three data-groups with the top 10, 20, and 40 most significant antigens were created. The five machine learning methods were performed for all three data-groups. The training accuracy and area under the ROC curve values were calculated. The models were validated with a 10-fold cross-validation and the accuracy of validated models was computed. Different values were examined for each method’s parameters to achieve the highest accuracy for the models. RF, SVM, and KNN obtained the highest accuracy for data-group 2 with the top 20 most significant antigens. LR and LDA obtained the highest accuracy for the top 10 most significant antigens group. RF (78.3%), SVM (73.3%), LR (72.7%), LDA (71.3%), and KNN (67.3%) respectively showed the best performance of the 10-fold cross-validation. Data-group 3 with the 40 most significant antigens did not show any considerable performance in terms of accuracy in comparison to other groups. In conclusion, performing machine learning methods on our data has resulted in the highest accuracy for the random forest model by using the top 20 most significant antigens.