Objective To noninvasively assess the cerebral ischemic status using the velocity profile of the carotid and vertebral arteries measured by Doppler ultrasound and a neural network model. Methods Imaging data were collected from patients who underwent computed tomography perfusion (CTP) and Doppler ultrasound. Hemodynamic parameters were extracted from the ultrasound images. These parameters were used to train a fully connected neural network model. The resulting model was validated using the CTP results. Results Sixty-two eligible patients were included; 44 were randomly selected as the training dataset and 18 were designated for validation. In the training set, the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy were 0.95, 0.833, 0.923, and 0.886, respectively. In the test set, the AUC, sensitivity, specificity, and accuracy were 0.860, 0.714, 1.000, and 0.889, respectively. Conclusions The model based on Doppler ultrasound and neural network was clinically verified and had good accuracy for assessing cerebral ischemia, showing its clinical potential for the early screening of cerebral ischemia.