Ab initio molecular dynamics simulations are an integral part of any electronic structure calculation to access thermal stability and perform non-adiabatic dynamics but are computationally very demanding. To enhance the computational efficiency of crucial ab initio molecular dynamics simulations, in this work, we implemented the graph neural network (GNN)-accelerated predictions for the molecular dynamics simulation of two-dimensional systems with varying atom connectivity. In this work, we developed an equivariant GNN model that employs only the time-evolved AIMD-simulated atomic coordinates for training and successfully predicts the key parameters of stable two-dimensional g-CN, WTe