Machine learning in the diagnosis of diabetes is a significant research area that focuses on leveraging computational models to improve early detection, risk prediction, and personalized treatment planning. Traditional diagnostic methods rely on clinical tests such as fasting blood glucose, HbA1c levels, and oral glucose tolerance tests, which may not always capture subtle risk patterns. Machine learning approaches analyze large-scale clinical, demographic, lifestyle, and genetic data to identify high-risk individuals and predict disease onset. Early research utilized classical algorithms such as support vector machines (SVM), decision trees, k-nearest neighbors (KNN), and random forests for diabetes classification and risk assessment. Recent studies employ deep learning models including artificial neural networks (ANNs), convolutional neural networks (CNNs) for structured and imaging data, recurrent neural networks (RNNs) for temporal health records, and hybrid architectures for multi-modal data integration. Applications include early detection, prediction of diabetic complications (e.g., neuropathy, retinopathy), and personalized treatment recommendation. Current research also explores explainable AI for clinical interpretability, feature selection, transfer learning, and federated learning for privacy-preserving collaborative diagnosis, establishing machine learning as a powerful tool for enhancing the accuracy, efficiency, and personalization of diabetes diagnosis and management.