An ineffective usage of insulin by the body is attributed to a hormone produced by the placenta in cases of gestational diabetes mellitus (GDM). Due to the potential for more effective therapies and less cumulative effects, early detection of GDM risk is crucial. Investigating an effective model for early GDM detection utilizing widely available characteristics in order to facilitate early intervention is the goal of this research. Where more extensive evaluations are not available, the initiative intends to employ prediction algorithms. With accuracy ranging from 76.2 to 90.5%, many classifiers are developed that perform well in the diagnosis of GDM. The highest accuracy of 90.5% was attained by the KNN and LightGBM classifiers out of all of them. According to this, a GDM detection system may be developed by using these classifiers. Numerous techniques are used by the classifiers, such as AdaBoost, gradients boosting, decision trees, CatBoost, random forests, Gaussian NB, LDA, logistic regression, LightGBM, and so on.