Diabetes is understood to be an ailment where the human being system's blood sugar amounts tend to be unusually higher. It's acknowledged all over the globe among the long-term problems. Diabetes prevents your body's capability to help to make insulin, leading to extreme blood sugar levels as well as gluconeogenesis abnormalities. A lot of women are influenced by gestational diabetes, the industry type of diabetes occurring throughout being pregnant. Ladies tend to be more likely compared to guys to build up diabetes-related difficulties, as well as women that are pregnant may create gestational diabetes throughout their pregnancy. The recent advancement of Machine learning (ML) provides a significant part in illness detection and prediction upon many phenomena. This makes ML great techniques to predict diabetic disease prediction. This research chose the well-known Logistic Regression (LgR), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forests (RF), XGBoost, and LightGBM, for diabetes prediction. A comparative study of the algorithmic performances is performed to identify the best valuable algorithm in the clinical decisions system. In the experiment, the LightGBM classifier gives the highest accuracy (88.5%) for the diabetes detection. Furthermore, this article has also compared the proposed work with existing state-of-art works. Results found that the proposed model gives better results than existing work.