In reacting flows, detailed chemistry computations are usually avoided precomputing the thermochemical quantities as functions of a reduced set of variables such as the Flamelet Generated Manifold (FGM) approach[34]. Although it mitigates the calculations of detailed chemical mechanism, the memory requirement associated to store the lookup table and retrieve the information during numerical simulations is usually large (order of Gigabytes). Thereby, extending the FGM approach in order to include other conditions requires to add other independent variables which will inevitable lead to increase the size of the lookup table. This will generate that Large Eddy Simulations (LES) cannot be performed such as is the case of the Diluted Air FGM (DA-FGM) approach developed by Xu Huang[9], limiting the simulations to Reynolds Average Navier-Stokes (RANS) approach. In this master thesis, the goal is to use Artificial Intelligence (AI)-Machine Learning (ML) techniques in order to reduce substantially the computational cost of storing lookup tables. In order to achieve this, the Artificial Neural Network (ANN) technique is used. First, a 4D FGM lookup table for hydrogen flames is simplified using the aforementioned AI technique. Then, this technique is used to replace a 6D lookup table generated using the DA-FGM approach. The accuracy and stability of the models provided by ANNs is measured by statistical indicators, providing high accurate and stable AI models. Finally, in the middle of this project, unexpected issues regarding the 4D lookup table were encountered, which lead to recreate the 4D lookup table. After studying carefully how the 4D lookup table was created, a new 4D lookup table is generated, providing excellent results and improving the AI models obtained.