PURPOSES : This paper is aimed at suggesting a novel approach for determining the pavement condition rating based on the tire-surface friction noise using a machine learning algorithm as a low-end pavement condition monitoring system. METHODS : Vehicle on-board type noise measurement system according to the ISO11819-2, and the K-nearest neighbors with dynamic time warping algorithm were applied. The system and algorithm were empirically tested with a field study. RESULTS : The developed AI- and noise-based pavement condition monitoring system demonstrated significantly positive results with a precision 90.8%, recall 84.8%, and f1-score 86.1%. CONCLUSIONS: We herein confirmed that the acoustic property between the tire and road surface can be used for monitoring pavement conditions. It is believed this finding presented a new paradigm for monitoring pavement conditions based on visual information. However, extensive studies focused on the practical application of this method are required.