Evaluation of the road's condition and state is essential for its upkeep, especially when discussing gravel roads, for the following reasons, among other. When loose gravel is not adequately maintained, it can pose a hazard to drivers, who can lose control of their vehicle and cause accidents. Current maintenance procedures are either laborious or time-consuming. Road agencies and institutions are on the lookout for more effective techniques. This study seeks to establish an automatic method for estimating loose gravel using acoustic observation. On gravelroads, recordings from a car's interior were evaluated and matched to the road's state. The first strategy examined road sections with a four-tier (multiclass) manual classification, based on their perceived condition of loose gravel, in accordance with the Swedish road administration authority’s guidelines. The second, examined two tier (binary) manual classification, distinguishing between roads with low and high maintenance needs. Sound features were extracted and processed for subsequentanalysis. Several supervised machine learning methods and algorithms, combined with selected data preprocessing strategies, were deployed. The performance of each strategy and model is determined by assessing and evaluating their classification accuracy along with other performance metrics. The SVM classifier had the best performance in classifying both multiclass as well as binary gravel road conditions. SVM achieved an accuracy of 57.8% when classifying on a four-tier scale and an accuracy of 82% when classifying on a two-tier scale. These results indicate some merits of using audio features as predictive features in the automatic classification of loose gravel conditions on gravel roads.