Diabetes is a persistent metabolic condition that affects millions of people globally. The effective management of diabetes care is crucial in order to prevent complications and improve patient outcomes. The application of machine learning methods for the field of medical care, particularly in the management of diabetes, has significantly increased in recent years. This review seeks to offer a thorough examination of machine learning techniques used in diabetes treatment, both supervised and unsupervised Algorithms for supervised machine learning have been widely used for a variety of diabetes care activities, including risk assessment, diagnosis, and medication recommendation. These algorithms utilize labelled data to train predictive models, allowing for accurate identification of high-risk individuals, early detection of diabetes, and personalized treatment plans. In particular, support vector machines, random forests, and synthetic neural networks have produced promising outcomes in these fields of contrast, unsupervised machine learning techniques have been used for pattern identification and exploratory analysis of big datasets without specified labels. The identification of patient subgroups based on shared traits using clustering techniques like k-means and hierarchical clustering has enabled personalised therapies and precision medicine approaches in the treatment of diabetes. In order to visualize complicated data and show hidden correlations, dimensionality reduction approaches such as principal component analysis as well as t-distributed stochastic neighbor embedding have been successful.