<p>The main objective of the conducted research is to present an application of computational fluid dynamics simulation technique to investigate the numerical solution of hybrid nanofluid flow in branched channel carotid arteries configurations with blood as a base fluid. This study examines blood flow across three different branched channel arteries configurations: lateral bifurcation, T-bifurcation, and Y-bifurcation. The numerical treatment of momentum and thermal boundary layer is examined using computational fluid dynamics. The significance of bifurcation shape and hybrid nanoparticles’ interaction on flow behavior and thermal performance are elaborated. The primary objective of computational fluid dynamics simulation with COMSOL is to demonstrate the variations in pressure and velocity profile due to blood flow in branched channel carotid arteries configurations. The artificial neural network algorithm is applied to obtain the predicted solution velocity and flow patterns. The comparison of the predicted solution with the artificial neural network algorithm and the numerical solution with COMSOL is provided. The graphical results demonstrate that T-bifurcation exhibits the maximum pressure and velocity, however, Y-bifurcation maintains the most efficient and consistent flow. This paper combines, in a unique way, ANNs with CFD simulations to obtain high accuracy of predictions in complex geometry velocity and patterns of flow has been validated against the results by using COMSOL. This bridge between machine learning and fluid dynamics, it provides valuable insights into optimizing channel shapes for enhanced flow efficiency. Future research would pursue turbulence modeling, pulsatile flow, and variations of Silver-Gold hybrid nanofluids, leading to higher heat transfer and lower resistance. Such discoveries possess great potential in biomedical applications. These range from cardiovascular drug delivery to surgical planning and diagnostic tools.</p>