Climate change has had a significant impact on honey bee production. For instance, droughts affect the availability of bees' food resources. Consequently, one emerging line of research involves the application of machine learning to model honey production. This paper presents a bibliographic review of machine learning research aimed at predicting honey yield. A comprehensive literature review was conducted using the primary scientific database known as WoS, followed by the systematization of information. The most noteworthy models identified are MARS (Multivariate Adaptive Regression Splines) and GBR (Gradient Boosting Regression). When applying MARS, the variables used include survey records, questions pertaining to social, economic, educational aspects, honey bee races, and production records. This model achieved a determination coefficient (r) of 0.920. With the GBR model, the main variables used relate to climatic records, image use, and harvest information. This technique can predict with an average error of ±10.3 kg for weight, with this weight being in the correct class 82% of the time. Current studies primarily employ more traditional statistics and machine learning techniques, with few exploring more innovative methodologies such as deep neural networks. Implementing these advanced algorithms poses a challenge, further compounded by the limited availability of unified databases for these developments, with information scattered across multiple sources.