Traditional diagnoses of addiction rely on the patients’ self-reports, which are easy to be dampened by false memory or malingering. Machine learning (ML) is a data-driven procedure that learns algorithms from training data and makes predictions. It is quickly developed and is more and more utilized in clinical applications, including diagnoses of addiction. This chapter reviewed the basic concepts and processes of ML. Some studies utilizing ML to classify addicts and non-addicts, separate different types of addiction, and evaluate the effects of treatment are also reviewed. Both advantages and shortcomings of ML in diagnosis of addiction are discussed.