Rutting is one of the most important deteriorations in flexible pavements and a significant amount of annual maintenance and rehabilitation funds are spent for its repair. Permanent deformations increases, which leads to increasing rut depth, can cause irrepairable problems in asphalt pavements. On the other hand, in Marshall method, which is known as the main method of asphalt mixtures mix design in Iran, the lack of a simple test to determine specimen resistance to permanent deformation is sensible. Although there are many devices to measure the rutting nowadays, but none of them have the ability to be used at site level. In addition, prevalent methods of evaluating rutting potential of asphalt mixtures are usually expensive and time consuming. The abovementioned factors necessitate development of a simple laboratory method that not only enjoys an acceptable precision but also is able to predict specimens rutting performance with low cost within the short time. In this research, one type of aggregate, one type of gradation, two types of bitumen, one type of filler, and three bitumen contents were used to prepare Marshall asphalt mixture specimens. After performing the main tests on specimens, IDT test results and Marshall parameters were used to develop a mathematical model to estimate specimen Wheel Track apparatus rut depth. The presented model measures rutting resistance using a combination of indirect tensile strength test results and Marshall parameters. The model is validated using artificial neural network, which makes it possible to evaluate rutting potential while OBC is being determined in a laboratory. Therefore, not only is there no need for expensive instruments of rutting test, but also a remarkable time saving in mix design procedure is achievable.