The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as "measurement"). The measurement literature has seen rapid growth in applications of ML to solve measurement problems. However, as we emphasize in this article, it is imperative to critically examine the potential risks associated with involving ML in measurement. The MxML project aims to explore the relationship between measurement and ML, so as to identify and address the risks and better harness the power of ML to serve measurement missions. This paper describes the first study of the MxML project, in which we summarize the state of the field of applications, extensions, and discussions about ML in measurement contexts with a systematic review of the recent 10 years' literature. We provide a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types of articles (e.g., applications, conceptual, etc.), (3) ML methods discussed, and (4) potential risks associated with involving ML in measurement, which result from the differences between what measurement tasks need versus what ML techniques can provide.