This research work aims to automate the prediction of peripheral arterial diseases implied by the Lower Extremity Arterial Doppler (LEAD) studies by applying machine learning and artificial intelligence algorithms. This study is the first to use machine learning and artificial intelligence algorithms to analyze LEAD data for peripheral arterial disease prediction. Specifically, we employ a Convolutional Neural Network (CNN) to classify the waveform into three types. The classified waveforms are used as input to the learning algorithms for disease prediction. We evaluate two traditional machine learning algorithms as well as two neural networks to predict normal and three types of artery diseases: aortoiliac disease, femoral-popliteal arterial disease, and trifurcation disease. The hierarchical neural network model (HNN) is investigated to deal with imbalanced data set. The first level of the HNN predicts the normal from diseases. The remaining two neural networks are used to predict other diseases from the rest. HNN has achieved high F1 scores: 99% on the normal case, 97% on the aortoiliac disease, and 94% on the femoral-popliteal arterial disease and 89% on the trifurcation disease through 10-fold cross-validation. The comparison shows that HNN works better than multilayer perceptron, random forests, and SVM. The overall result demonstrates that machine learning and artificial intelligence algorithms can be developed for peripheral arterial diseases implied by the LEAD studies while reducing the reading variability in vascular laboratories.