Disruptive technology, especially machine learning (ML), is changing the paradigm in many fields, including quality. Advancements in data science, increasing processing powers of computers, and the availability of massive datasets, have made machine learning a useful tool to solve the problem at scale. In this work, a systematic review of literature has been conducted to analyze the type of industry and quality problems that can be detected with ML. ML applications in industries such as service, manufacturing, food, software/IT, and healthcare to detect quality issues and detect fraud in healthcare and health insurance have been presented. The paper has also summarized the common themes in applying ML in detecting quality problems and discussed the advantages and disadvantages of various ML algorithms in detecting quality issues and anomalies, including fraud, in various industries.