The simulation of reactive flows is a major challenge in several industrial sectors, such as aeronautics or energy production. The coupling between fluid dynamics and chemistry comes however at a cost, as chemical processes involve a wide range of spatial and temporal scales. The resulting equations are stiff and require specific, and expensive, numerical methods. The use of machine learning to estimate the reaction rates has been recently proposed. In particular, Artificial Neural Networks (ANN) have the ability to perform interpolation on high-dimensional data and are thus particularly adapted to chemistry problems. A major issue is then to select an appropriate database on which to train the ANN. It must: (i) be representative of the targeted application; (ii) be sufficiently quick to generate. A promising strategy is to use 0-D stochastics reactors, which mimic reactive and mixing processes in systems while being cheap to compute. This methodology has been successfully applied to non-premixed combustion in the literature. In the present work, the aim is to investigate the ability of the 0D stochastic reactors to be used as a database for a wider range of combustion systems. More specifically, the focus will be on the ability to predict auto-ignition followed by premixed flame propagation. To that purpose, a 2-D turbulent case involving the auto-ignition of a hotspot in a hydrogen/air mixture and the subsequent propagation of a premixed flame is proposed. An ANN model based on stochastic reactors is then built and tested on (i) a 0-D auto-ignition case; (ii) a 1-D laminar premixed flame propagation; (ii) the full 2-D turbulent configuration. Using adequate data transformation at the input and output of the neural network, accurate results are obtained, highlighting the ability of the proposed strategy to deal with a large range of combustion applications.