Modeling the atmospheric long-range propagation is an important step in the development of observation, satellite, or communication systems. This paper presents a parallelized version of the two-way split-step wavelet method, designed to enhance computational efficiency. Numerical experiments in the UHF band are provided to highlight the benefits of the proposed method. In the studied cases, we observe a 25%-60% gain in terms of computation time, depending on the terrain taken into account. The proposed approach is also leveraged to generate a dataset for training a machine-learning model based on the U-Net architecture.