 Gestational diabetes is a type of diabetes that occurs in some women during pregnancy. It can lead to complications for both the mother and child, so it is important to identify cases early on. Machine learning algorithms have been developed to help predict cases of gestational diabetes, reducing the amount of data needed to make accurate predictions. This hybrid approach combines clustering techniques with classification models like decision trees, random forests, support vector machines, SVM, k-nearest neighbors, KNN, logistic regression, and naive bays. Results showed that accuracy increased when clustering and classification were combined. This article was authored by Rasool F. Jader, Sadeg Aminafar, and Madhav for Haji MABD.