In this work, we have developed a deep learning approach for Doppler angle estimation. The clinical goal is to replace the repetitive and sometimes inconsistent manual Doppler angle adjustments. The chosen angle greatly affects the accuracy of blood flow velocity measurements, which in turn can have a major effect on the diagnosis and grading of arterial stenosis. The mean difference between the manual measurement of Doppler angle and the values estimated by the automated algorithm was computed. The results indicate that the mean difference was in the range of XXX to XXX degrees (for the different deep learning models evaluated) over the entire range of observed Doppler angles. However, in the range of Doppler angles 60 to 120 degrees, the mean difference was even smaller (XXX (SD=XXX)). This is attractive clinically because most ultrasound exams are performed with the carotid artery in the longitudinal orientation where the vessel is approximately perpendicular with the vertical axis. Hence, a clinical implementation of this algorithm would require that angles around 90 degrees are most accurately detected as demonstrated in this study.