In this study, we present a novel approach to high-fidelity wind turbine wake prediction using convolutional neural networks (CNN)-based superresolution techniques. Wind turbine wakes, which significantly impact the efficiency and lifespan of downstream turbines, have traditionally been challenging to model accurately due to their complex, dynamic nature. By employing CNNs to enhance low-resolution computational fluid dynamics (CFD) simulations, our method substantially improves the spatial resolution and predictive accuracy of wake profiles. This advancement allows for more precise predictions of wake characteristics, such as velocity deficits and turbulence intensity, over a range of atmospheric conditions.