Abstract: Machine learning technique is important for assisting the energy transition and promoting the renewable energy consumption. In recent years, the application of machine learning technique in power systems has been widely concerned. Due to the‘black box’nature of machine learning technique, its interpretability and robustness are still to be improved. And there is a certain contradiction with the operation requirements of high reliability in the power system, which leads to its practical engineering application lagging behind the theoretical research. In order to introduce the practical application of machine learning technique, this paper focuses on the field of power distribution in North America. The typical engineering practice projects of machine learning technique are summarized from the perspectives of source, network and load, and the method, effect and inspiration of each project are also outlined. Further, the above projects are classified into two categories, i.e., situational awareness and decision support,and a total of five application scenarios. And the research areas of machine learning technique in the next stage is analyzed from the perspective of engineering practice.