Gestational Diabetes Mellitus (GDM) is a serious health issue and could cause complications to the mother and child in case it is not identified and treated in time. Conventional diagnostic tools, including metabolomics, transcriptomics, and epigenetics, are usually not standardized and have a high degree of variation across studies. Such approaches are also hampered by non-diversity in the population of study, that restricts reliability. This research will overcome these limitations by taking real-time maternal diagnostic data and creating a powerful machine-learning-based method to determine the most impactful biomarkers to predict GDM. The flow of the work is based on the systematic preprocessing of data that guarantees quality and consistency. The Machine Learning classifiers including, Logistic Regression, Random Forest, Support Vector Classifiers (SVC), ExtraTrees Classifiers and Gradient Boosting Classifiers were used together with advanced feature selection methods Lasso Regression, SelectKBest, Recursive Feature Elimination (RFE), and SelectFromModel to identify the important biomarkers of GDM. Hyperparameter optimization and validation with successive iterations led to the optimization of the model and reliable predictions. The overall methodology of the study allowed determining the presence of critical maternal diagnostic features that influence GDM. The research offers useful information on the best biomarkers of GDM by integrating data preprocessing, feature selection, and model assessment. This strategy has potential in enhancing the early diagnosis of GDM and highlights the possibilities of machine learning in filling the gaps that are expensive to overcome in conventional diagnostic methods.