The recent surge of machine learning (ML) has impacted many disciplines, including educational and psychological measurement (hereafter shortened as measurement, “M”). The measurement literature has seen a rapid growth in studies that explore using ML methods to solve measurement problems. However, there exist gaps between the typical paradigm of ML and fundamental principles of measurement. The MxML project was created to explore how the measurement community might potentially redefine the psychometrics discipline in the imminent future of big data and machine learning, so as to harness the power of machine learning to serve our (redefined and updated) mission. 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 methods in measurement contexts with a systematic review of the recent 10 years of literature (2013 - 2022). Specifically, we provide a snapshot of the literature in terms of (1) areas of measurement, (2) types of article, (3) ML methods discussed, and (4) gaps addressed between measurement goals and ML methods.