Atmospheric optical turbulence is the fundamental parameter closely related to the design and application of optoelectronic systems. The field measurements of atmospheric optical turbulence profiles by instruments are limited by labor, materials, financial resources, and other conditions. Therefore, it is of great significance to estimate the intensity of atmospheric optical turbulence according to the conventional meteorological parameters. A back propagation combined with genetic algorithm (GA-BP) neural network is proposed.First, based on Tatarski atmospheric optical turbulence parameterization scheme, the HMNSP99 outer-scale model is used to estimate the optical turbulence profiles; second, attempting to construct BP artificial neural network combined with genetic algorithm, which are trained by measured data to predict atmospheric optical turbulence profiles. The atmospheric optical turbulence profiles estimated by the two methods are compared with the measured profiles. The results show that the root mean square error (RMSE) between the estimated values of GA-BP neural network and measured values is smaller than that of HMNSP99 model, which proves that it is a feasible method to use GA-BP artificial neural network model to estimate the optical turbulence profiles.