Nitrogen cycle is a complex process of multi-media and multi-interface between water-soil-atmosphere-biology in the Earth's sphere, which is closely related to environmental problems such as soil health, food security, global warming, air pollution and water quality. With the rapid development of computer technology and the generation of massive and multi-source data in recent years, machine learning (ML) has rapidly become a powerful tool to study nitrogen cycle. This paper first introduces the functional concepts of ML, including typical development process and learning application scenarios. Then typical application algorithms of ML are summarized, including classical ML (such as random forest, support vector machine, etc.) and deep learning (such as convolutional neural network, long-term and short-term memory network, etc.). In addition, the application research progress of ML in the field of nitrogepn cycle research are reviewed, including nitrogen metabolism mechanism, simulating nitrogen cycle process and managing nitrogen flow in atmosphere, water, soil and plant/crop. In the future, the research of feature engineering and model fusion based on big data and ML technology will bring great changes to data analysis and modeling in the field of nitrogen cycle. Meanwhile, combine ML with process-based models to solve complex problems in the nitrogen cycle, which will provide important support for serving the national "double carbon" strategy and controlling global warming, air pollution and other environmental issues.