Laser based communication and weapons systems are integral to maintaining the operational readiness and dominance of our Navy. Perhaps one of the most intransigent obstacles for such systems is the atmosphere. This is particularly true in the near-maritime environment. Atmospheric turbulence perturbs the propagation of laser beams as they are subject to fluctuations in the refractive index of air. As the beams travel through the atmosphere there is loss of irradiance on target, beam spread, beam wander, and intensity fluctuations of the propagating laser beam. The refractive index structure parameter, C2n, is a measure of the intensity of the optical turbulence along a path. If C2n can be easily and efficiently determined in an operating environment, the prediction of laser performance will be greatly enhanced. The goal of this research is to use image quality features in combination with machine learning techniques to accurately predict the refractive index structure parameter, C2n. In order to construct a machine learning model for the refractive index structure parameter, a series of image quality features were evaluated. Seven image quality features were selected, and have been applied to an image dataset of 34,000 individual exposures. This dataset, along with independently measured C2n values from a scintillometer as the supervised variable, were then used to train a variety of machine learning models. The models of particular interest to this research are the Generalized Linear Model, the Bagged Decision Tree, the Boosted Decision Tree, as well as the Random Forest Model. While the quantity of available training data had a significant impact on model performance, the findings indicate that image quality can be used to assist in the prediction of C2n, and that the machine learning models outperform the linear model.