Predicting Diabetes Complications Using Machine Learning Algorithms Diabetes mellitus is one of the most widespread chronic diseases worldwide and is associated with severe complications, including nephropathy, retinopathy, cardiovascular diseases, and limb amputation. Early prediction of these complications has become a major focus in modern medical research. With the rapid advancement of machine learning techniques, large volumes of clinical data can now be analyzed to identify patterns that may indicate the progression of complications before they become clinically evident. Machine learning algorithms rely on patient data such as blood glucose levels, HbA1c, blood pressure, body mass index, disease duration, and other laboratory findings. Commonly used algorithms include logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks in deep learning models. These approaches enable the development of predictive systems capable of classifying patients according to their risk levels. Machine learning methods often outperform traditional statistical techniques, particularly when handling complex, high-dimensional medical datasets. These predictive models can be integrated into electronic health record systems to assist physicians in personalized treatment planning. Leading medical research institutions, such as Mayo Clinic, have adopted advanced data analytics to develop predictive tools for chronic diseases. However, challenges remain, including data quality issues, class imbalance, and patient privacy concerns. Model interpretability is also crucial in clinical settings to ensure physician trust and ethical deployment. Overall, applying machine learning algorithms to predict diabetes complications represents a promising step toward precision medicine and improved healthcare outcomes.