This paper presents a comparative assessment of surrogate models in probabilistic analysis of finite plain journal bearing. Traditional Monte Carlo simulation (MCS) is employed dealing with a large number of data for validating the constructed surrogate model dealing with a limited number of data. In the case of a complex engineering system like journal bearing with finite length wherein no analytical solution exists and the system needs to be solved based on expensive numerical techniques such as finite difference and finite element method due to unavailability of a large number of experimental data. Hence, surrogate models show their computational efficiency complying with the accuracy of the models. Thus, the present study aims to investigate the applicability of five surrogate models such as moving least square, support vector machine, radial basis function, polynomial neural network and multivariate adaptive regression splines in terms of their efficiency and accuracy. A probabilistic analysis approach combining the finite difference method, surrogate models and MCS is presented in this work. The validation and parametric results corresponding to the comparison of the constructed surrogate models are presented. Substantial intuitive new results are conferred in the probabilistic surrogate schemes.