The main objective of this work is to develop models using multivariable regression and artificial neural network approaches for cost estimation of the construction costs of trackworks of light rail transit and metro projects at the early stages of the construction process in Turkey. These two approaches were applied to a data set of 16 projects by using seventeen parameters available at the early design phase. According to the results of each method, regression analysis estimated the cost of testing samples with an error of 2.32%. On the other hand, artificial neural network estimated the cost with 5.76% error, which is slightly higher than the regression error. As a result, two successful cost estimation models have been developed within the scope of this study. These models can be beneficial while taking the decision in the tender phase of projects that includes trackworks.