Background: The purple sweet potato, Ipomoea batatas, belongs to the family Convolvulaceae. It is one of the most widely consumed tubers in Asia and is found in many dishes. Many people with diabetes eat purple sweet potato tubers to help reduce blood glucose in China. Objective: To predict the ultrasonic conditions for getting the optimal in vitro antioxidant and antiglycated activity of ultrasonic extracted polysaccharides from purple sweet potato (I. batatas) tubers, the artificial neural network (ANN) regression models was used in this study. Materials and Methods: The antioxidant activity of polysaccharides was quantified by evaluating the hydroxyl radical scavenging effect after ultrasonic extraction, and the data were used in conjunction with optimized extraction conditions to train the predictive ANN models. Results: The following conditions were predicted to yield optimal hydroxyl scavenging activity: 200 W, 22°C, and 40 min. In contrast, conditions of 230 W, 22°C, and 50 min yielded the greatest inhibitory effect on albumin nonenzymatic glycosylation. The accuracy and predictive ability of the models ranged from good to excellent, as indicated by R2 values ranging from 0.953 to 0.998. Conclusion: The results of the present study showed that ANN predictive models are useful in ultrasonic processing, which can rapidly and accurately predict the optimum extraction conditions for polysaccharides based on their antioxidant and antiglycated activities. In addition, the results of the present study suggest that the consumption of sweet potatoes may help reduce free radicals in the body and prevent or treat diabetes.