The cerebral microvasculature plays a critical role in maintaining brain homeostasis, yet its complex structure and dynamics pose significant challenges for mathematical modeling and simulation [1]. In this work, we investigate the potential of Graph Neural Networks (GNNs) to simulate microcirculatory blood flow, focusing on applications to the cerebral microvasculature. GNNs, a class of machine learning models adept at processing non-Euclidean data, are particularly well-suited for analyzing vascular networks, which are inherently graph-structured. Our study introduces a novel methodology that combines synthetic vascular network generation with GNN-based predictions of blood flow dynamics. The synthesis of vascular networks is a strategy for systematically generating a sufficient volume of “ground truth” training data for a GNN model. Synthetic networks serve as the basis for flow simulations using both linear and nonlinear solvers [2], depending on what rheological models are considered. The resulting data sets are then used to train GNN models tailored to predict key hemodynamic variables, including pressures, blood velocities, and hematocrit levels. We develop and evaluate several GNN models with increasing complexity, addressing challenges such as variability in capillary blood velocities, the accuracy of hematocrit level predictions, and the ability to generalize across larger and more complex vascular networks. Testing on networks modeled after the mouse cerebral cortex [3] revealed promising results, particularly in the accurate prediction of pressure distributions. These findings highlight the robustness of GNNs in capturing the nuanced dynamics of microcirculatory flow when supplied with training data generated from highly detailed synthetic network structures. This work underscores the potential of GNNs as a transformative tool for studying microcirculation. By offering precise simulations of cerebral blood flow, this approach opens new avenues for understanding microvascular physiology and pathology, with implications for diseases such as cerebrovascular and neurological disorders.