Diabetes is a widespread chronic disease, and early prediction can aid in effective management and treatment. An efficient and accurate prediction model for diabetes is highly desirable as the increasing prevalence of diabetes is posing a significant global health challenge. This study offers a thorough summary of the state of research in this field and explores the use of different machine learning (ML) algorithms for diabetes prediction. By examining algorithms such as support vector machine (SVM), decision tree (DT), neural networks, and ensemble methods, this review critically evaluates their strengths, weaknesses, and suitability for diabetes prediction. By critically analyzing the strengths and limitations of each algorithm, this review offers insights into their predictive performance, interpretability, and scalability. Furthermore, it discusses the diverse features and data sources utilized in diabetes prediction, exploring their impact on model accuracy and generalization. Finally, researchers, clinicians, and other stakeholders interested in leveraging ML to improve diabetes early detection and care will find great guidance from this summary of the body of research.