Machine learning and data mining methods are increasingly used in diabetes research to analyze large datasets and extract valuable knowledge. The study reviews the application of these methods in diabetes research, focusing on prediction and diagnosis, diabetic complications, genetic background and environment, and healthcare management. Machine learning algorithms, particularly support vector machines (SVM), are widely used for biomarker identification and prediction of diabetes. Supervised learning approaches are more common, with 85% of the algorithms used in the study being supervised. Clinical datasets are mainly used, and the study highlights the importance of feature selection in improving model performance. The study also discusses the use of data mining techniques in predicting diabetic complications, such as diabetic neuropathy, nephropathy, and retinopathy. Machine learning is also applied in drug discovery and therapy, with various algorithms used to predict drug responses and optimize treatment plans. Genetic factors and environmental influences are also explored, with studies identifying genetic markers and environmental risk factors associated with diabetes. The study emphasizes the importance of data mining and machine learning in improving diabetes diagnosis, management, and treatment. The review concludes that these methods are essential for understanding and managing diabetes, and further research is needed to improve their application in clinical practice.Machine learning and data mining methods are increasingly used in diabetes research to analyze large datasets and extract valuable knowledge. The study reviews the application of these methods in diabetes research, focusing on prediction and diagnosis, diabetic complications, genetic background and environment, and healthcare management. Machine learning algorithms, particularly support vector machines (SVM), are widely used for biomarker identification and prediction of diabetes. Supervised learning approaches are more common, with 85% of the algorithms used in the study being supervised. Clinical datasets are mainly used, and the study highlights the importance of feature selection in improving model performance. The study also discusses the use of data mining techniques in predicting diabetic complications, such as diabetic neuropathy, nephropathy, and retinopathy. Machine learning is also applied in drug discovery and therapy, with various algorithms used to predict drug responses and optimize treatment plans. Genetic factors and environmental influences are also explored, with studies identifying genetic markers and environmental risk factors associated with diabetes. The study emphasizes the importance of data mining and machine learning in improving diabetes diagnosis, management, and treatment. The review concludes that these methods are essential for understanding and managing diabetes, and further research is needed to improve their application in clinical practice.