In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.