Forest growth and yield prediction is an important field of forest management science, and modelling forest growth and yield is key to forest management decision-making. The traditional statistical growth models such as linear and nonlinear regression model, mixed-effect model, quantile regression, variable-in-error model are often applied under certain statistical assumptions, such as the data are independent, normally distributed and homoscedastic. The above requirements are usually difficult to be met for forest data with repeated observation and hierarchy. With the development of AI techniques, machine learning provides a new way for forest growth modeling, with the advantages of no requirements on data distribution, extracting deep knowledge from the data, and high accuracy. The applications in forest growth and yield are still less than other domains. We reviewed the main machine learning algorithms including classification and regression tree (CART), multivariate adaptive regression splines (MARS), bagging regression, boosted regression tree (BRT), random forest (RF), artificial neural networks (ANN), k
-nearest neighbors (k
-NN), and support vector machine (SVM), parameter tuning, software, advantages and challenge. We conclude that machine learning would be widely applied with great potential and its combination with traditional statistical methods would become a trend in forest growth and yield prediction.