Diabetes is a prevalent chronic disease with significant implications for public health. Predicting hospital readmission for diabetes patients plays a vital role in improving patient outcomes and resource allocation. This paper explores the application of machine learning algorithms to predict diabetes patient readmission. By leveraging the "Diabetes 130-US hospitals for years 1999-2008" database, various algorithms including K-nearest neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), Genetic Algorithms, Fuzzy, Decision Trees, Random Forest, and XGBoost are utilized to develop predictive models. The models are evaluated based on accuracy, precision, recall, and F1 score, with results exceeding 70% accuracy. The findings demonstrate the potential of machine learning algorithms in accurately identifying patients at risk of readmission. Implementing these models can aid in targeted interventions and proactive patient care, contributing to improved healthcare outcomes in diabetes management. Further research is needed to validate these algorithms in real-world clinical settings and enhance their robustness and generalizability.