ABSTRACT This paper proposes the development of an analytical-based Artificial Neural Network (ANN) model for predicting the concrete segmental lining parameters and comparing it with the finite element method, Rocscience tool. To achieve this aim, the dataset was prepared for twelve different loading conditions using the analytical equations by Muir Wood and Curtis. Using this dataset, an ANN model was developed for predicting the segmental lining parameters, viz. displacement, bending moment, axial and shear force. Furthermore, the Rocscience tool was used to analyse the aforementioned design parameters, and the results of these two methods were compared and summarised. It is found that the developed ANN model shows reasonable results of liner design parameters with the Rocscience program. The efficiency of both techniques was evaluated using the statistical measures viz. Mean Absolute Error, Root Mean Square Error, and Coefficient of Efficiency. It is observed that the ANN model is best fitted for the prediction of liner shear and axial force, while it could not find the optimised solution for liner displacement and bending moment. The finding of this study showed that the developed ANN model can be applied to predict the tunnel liner design parameters instead of the alternative complicated and time-consuming techniques.