Family farming is essentially characterized by the use of family labor force, due to the lack of land, water, and capital resources. An important tool is which allows them to know which products will be the best priced when production is completed, and at this point machine learning technology has, in particular, models and algorithms that allow for price prediction. The aim of this work is to review the literature related to price prediction of agricultural products using machine learning technology with the purpose of identifying the prediction models used in the studies. It also aims to identify the agricultural products used in these predictions to discuss their application in other products. The results show that neural network model is the most used in the selected studies.