Emulating complex problems of fracture mechanics requires the use of existing, or newly developed high-fidelity models. These models typically work by solving intricate systems where computational costs and time requirements scale up with problem complexity. A possible solution to circumvent these challenges involves reduced-order modeling techniques, such as Machine Learning (ML). A recently developed ML method for emulating large-scale complex physics while reducing computational costs is Graph Neural Networks (GNNs). GNNs work by integrating supervised ML along with graph theory. This work develops a GNN based framework to simulate fracture and stress evolution in brittle materials due to multiple microcracks’ interaction. The GNN framework is trained on the dataset generated by XFEM-based fracture simulator. The framework consists of four GNNs: the first prediction stage determines Mode-I and Mode-II stress intensity factors (which can be used to compute the stress evolution), the second prediction stage determines propagating microcracks, and the final stage propagates crack-tip positions for the selected microcracks to the next time-step. Our framework achieves good prediction accuracy compared to an XFEM-based fracture simulator. The trained GNN framework is capable of emulating crack propagation, coalescence and corresponding stress distribution for a wide range of initial microcrack configurations (from 5 to 19 microcracks) without any additional modifications. Lastly, the framework’s simulation time shows speed-ups 6x-25x faster compared to an XFEM-based simulator.