Limited research has been performed in testing and measuring the reinforcement corrosion levels using non-destructive tests. This research applied ultrasonic-based non-destructive test and artificial neural network to the diagnosis and prediction of rebar’s non-uniform corrosion-induced damage within reinforced concrete members. Ultrasonic velocities were tested by applying ultrasonic to reinforced concrete prisms before and after the rebar corrosion. Input parameters including concrete strength, ultrasonic velocity, and the specimen dimension-related variable were used for the prediction of reinforcement corrosion level adopting artificial neural network models. Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model. This study leads to future research in high-accuracy non-destructive measurement of reinforcement corrosion in concrete.