Diabetes is one of the major diseases of the population across worlds. Diabetes is a chronic disease that occurs either when the pan- crease does not produce enough insulin or when the body cannot efficiently use the insulin it produces. Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning algorithms to predict type 2 diabetes. However, each work uses different datasets and evaluation metrics for algorithms’ evaluation, making it difficult to compare among them. Machine learning is one of the branches of artificial intelligence. It focuses on creating systems that learn data and acquire knowledge to improve their performance automatically and without using programming directly. Machine learning relies on algorithms and models that allow systems to analyze data, gain experience, and make decisions, enabling them to adapt to tasks and improve their performance with time passing. Machine learning algorithms have helped health professionals (including doctors) treat, analyze and diagnose medical problems, as well as detect disease patterns and other patient data. Machine learning can help people make an initial judgment about diabetes according to daily physical examination data, and can serve as a reference for doctors.