The ability to classify cataracts is an important aspect due to the difference in managing a mature cataract and an immature cataract. This paper focuses on using different techniques in machine learning to address the general issue of automation in classification. This work uses logistic regression, and its modified version with L1 and L2 penalties, as well as other classifiers such as kNN, SVM, and random forest. Every model is optimized and validated on the dataset containing clinical and image-based attributes related to cataract maturity to determine the most appropriate model for classification. An assessment of these algorithms was made with the measures being accuracy, precision, recall, F1-score, and ROC AUC. Logistic regression presented baseline performance and advantages concerning the interpretability of features. To reduce the likelihood of over-fitting, regularization techniques were used to train the logistic regression model on how to perform better on new data. As evidenced in the findings from the experiments, the regularized logistic regression models yield higher accuracy and reliability as compared to other algorithms tested; it excels in identifying intricate patterns of the given set of data. The results showed that regularized logistic regression outperformed the unregularized counterparts through reduced variance in high-dimensional feature space.