<p>With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications, an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes. These models leverage the ability to capture complex, high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models. As a result, data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output. This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches. It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature. Subsequently, the related studies are categorized into four key areas: wind turbine power prediction, data-driven analytic wake models, wake field reconstruction, and the incorporation of explicit physical constraints. The accuracy of data-driven models is influenced by two primary factors: the quality of the training data and the performance of the model itself. Accordingly, both data accuracy and model structure are discussed in detail within the review.</p>