In the present study syngas-air diffusion flames are simulated using LES with artificial neural network (ANN) based chemical kinetics modeling and the results are compared with previous direct numerical simulation (DNS) study, which exhibits significant extinction-reignition and forms a challenging problem for ANN. The objective is to obtain speed-up in chemistry computation while still having the accuracy of stiff ODE solver. The ANN methodology is used in two ways: 1) to compute the instantaneous source term in the linear eddy mixing (LEM) subgrid combustion model used within LES framework, i.e., laminar-ANN used within LEMLES framework (LANN-LEMLES), and 2) to compute the filtered source terms directly within the LES framework, i.e., turbulent-ANN used within LES (TANN-LES), which further dicreases the computational speed. A thermo-chemical database is generated from a standalone one-dimensional LEM simulation and used to train the LANN for species source terms on grid-size of Kolmogorov scale. To train the TANN coefficients the thermo-chemical database from the standalone LEM simulation is filtered over the LES grid-size and then used for training. To evaluate the performance of the TANN methodology, the low Re test case is simulated with direct integration for chemical kinetics modeling in LEM subgrid combustion model within the LES framework (DI-LEMLES), LANN-LEMLES andTANN-LES. The TANN is generated for a low range of Ret in order to simulate the specific test case. The conditional statistics and pdfs of key scalars and the temporal evolution of the temperature and scalar dissipation rates are compared with the data extracted from DNS. Results show that the TANN-LES methodology can capture the extinction-reignition physics with reasonable accuracy compared to the DNS. Another TANN is generated for a high range of Ret expected to simulate test cases with different Re and a range of grid resolutions. The flame structure and the scalar dissipation rate statistics are analyzed to investigate success of the same TANN in simulating a range of test cases. Results show that the TANN-LES using TANN generated fora large range of Ret is capable of capturing the extinction-reignition physics with a very little loss of accuracy compared to the TANN-LES using TANN generated for the specific test case. The speed-up obtained by TANN-LES is significant compared to DI-LEMLES and LANN-LEMLES.