Modern information systems are increasingly used in various areas of our life. One of these is the quality control of the condition of the road surface in order to carry out repair work on time if necessary. The machine learning method can facilitate the control process, which was demonstrated in this work. Analyzing the road surface condition using image classification requires much pre-classified data and decent computing power. As the modern need for proper quality control of the road surface is high, it is possible to analyze using sensor-recorded data in tabular form and machine learning methods, which should show high accuracy of the classification results. Development and research of an information system for classifying the condition of the road surface were described in this paper, including ways for optimizing similar approaches and improving the results obtained through the use of a greater number of features, in particular, taking into account not only the speed indicators at the given time of the car's movement but also the performance indicators of internal combustion engine. As a result, an information system was developed that classifies the road surface condition using features obtained from various types of sensors and recorded in tabular form. Machine learning methods such as Random Forest, Decision Tree, Support Vector Method, and AutoML library were used to compare accuracy results using a large set of artificial intelligence methods. The best results were obtained using the Random Forest ensemble machine learning method. The analysis of the classifier according to various parameters was carried out, and a search for the best hyperparameters was performed. At the same time, achieving a 91.9% accuracy of road surface condition classification was possible. Parallel calculations were used during model training. As a result, training time was decreased by 5 times with the use of the CPU and by 51 times with the help of the GPU.