Forecasting atmospheric dispersion in complex configurations is a current challenge in fluid dynamics in terms of calculation time and accuracy. CFD models provide good accuracy but require a great computation time. Simplified or empirical models are designed to quickly evaluate the dispersion but are not adapted to complex geometry. Cellular Automata coupled with an Artificial Neural Network (CA-ANN) are developed here to calculate the atmospheric dispersion of methane (CH4) in 2D. Efforts are made in reducing computation time while keeping an acceptable accuracy. A CFD simulations database is created and the Advection-Diffusion Equation is discretized to provide variables for the ANN. Neural network design is made thanks to best sampling selection, architecture selection and optimized initialization. The coefficient of determination is over 0.7 for most cases of the test set despite small errors accumulated through time steps. CA-ANN is faster than CFD models by a factor from 1.5 to 120.