Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine learning and feature selection techniques to identify biomarkers for Type 2 Diabetes that can later be used for diagnosis and prediction. The main methods that we explored were Random Forests,Linear Regression, Support Vector Machines andXGBoost along with mRMR and CMIM as feature selection techniques. These methods were applied to data taken from Europe and China. We found that mRMR improved the performance of the Random Forest classifier compared to CMIM.Apart from finding biomarkers specific to one location, we found that Clostridiales, Clostridium, Roseburia and Lactobacillus could be of interestin the prediction of Type 2 Diabetes irrespective of location. This study verified biomarkers found in previous literature and evaluated several techniquesfor the prediction of the disease across different regions.