Accurately capturing unsteady flow phenomena and complex fluid dynamics is essential for understanding and predicting arterial blood flow behavior. This study leverages computational fluid dynamics (CFD) and machine learning (ML) to detect regions of elevated shear stress in the aorta, focusing on the dynamic responses of arteries under varying hemodynamic conditions. The geometric model of the human aorta was sourced from the Vascular Database, which is supported by SimVascular and includes all necessary boundary conditions. Simulations were performed using the Navier-Stokes equations within SimVascular to generate vtk files containing velocity and pressure data. Since these files could not be directly used for ML training, additional post-processing was conducted in ParaView. By applying specific functions, we extracted key metrics such as pressure, velocity, wall shear stress, and other parameters at multiple spatial coordinates. This resulted in a CSV dataset comprising 23,777 points with corresponding attributes for further analysis of high wall shear stress regions. A Random Forest classifier was trained on this dataset to predict regions of high wall shear stress of the aorta by analyzing attributes like pressure, velocity, and wall shear stress, providing precise coordinate-based predictions of areas with abnormal hemodynamic stress. In our analysis, regions of elevated shear stress were detected at 1,288 points, representing 5.42% of the total dataset. By integrating CFD with ML, we successfully identified regions of high wall shear stress in the aorta, enhancing clinical diagnostic accuracy and offering a data-driven alternative to traditional cardiovascular diagnostic techniques. This study underscores the value of combining CFD and ML to reduce reliance on traditional cardiovascular tests, streamlining diagnosis and therapeutic planning while reducing costs and complexity.