<p>Type 2 Gestational Diabetes Mellitus (GDM) is a chronic, non-communicable condition influenced by genetics as well as lifestyle choices such an unhealthy diet, being overweight, smoking, and insufficient exercise. Early detection of diabetes mellitus helps people to live longer, healthier lives, and prevents further health issues. In the modern healthcare system, artificial intelligence tools are utilized to automate disease identification. Machine learning approaches assist clinicians to detect diabetes mellitus early after gathering patient data. In order to identify GDM early, the classifier in this study is trained using the PIMA Indian data set from the UCI library. This research proposed an improved hybrid sampling and slime mould bio inspired algorithm-based machine learning technique for GDM identification. It is divided into three stages. First stage involves pre-processing the dataset and training the machine learning classifier without sampling. In the second stage, the sampling strategies are used to create a balanced dataset and the machine learning classifier is trained for prediction. In the third stage, the optimal sampling strategy is chosen, and the performance of the machine learning methodology is fine-tuned using slime mould meta heuristic optimization. For this research, popular sampling techniques SMOTE, SMOTE + EN, SMOTE + ENC, SMOTE + TOMEK, and SMOTE + ENN, are utilized to address the challenges associated with binary class imbalance learning. For machine training, the seven popular classifiers are used: Naive Bayes (NBC), Logistic Regression (LRC), Linear SVC (LSVC), Random Forest (RFC), K Nearest Neighbour (KNN), Decision tree (DTC), and Extra Tree classifier (ETC) are utilized. According to the results of this experiment, the hybrid SMOTE + ENN with slime mould meta heuristic tuning of the ETC classifier has a greater accuracy (98.3%) than other models.</p>