Gaussian processes are powerful tools to model sophisticated tasks in the machine-learning field. On the other hand, wax precipitation is an important problem in oil production and transportation operations. In this article, a Gaussian process regression is used to develop a prediction model to estimate wax precipitation in crude oil reservoirs. Then, performance of the proposed model is evaluated. In addition, the authors' model is compared with the most well known previous work (multi-solid model) and it is shown that the authors' model outperforms it in terms of accuracy and generality.