TemPoRe is an algorithm implemented in Python that combines traditional methods with a machine learning technique to track tempo in real-time. To this end, the autocorrelation function is calculated on the incoming signal to detect tempo and polynomial regression is utilized to predict the next tempo based on the prior tempo to obtain a more stable tempo. This paper will demonstrate how supervised learning can improve the accuracy of real-time tempo tracking and additionally suggest ways to further enhance the performance of this algorithm.