Objective Estimating the joint roughness coefficient (JRC) is essential for evaluating the mechanical properties of a rock mass. Due to the limitation of a single statistical parameter for characterizing morphology, JRC values estimation by a single statistical parameter may produce a sufficiently unreliable result. Methods To address the existing challenges in determining JRC values, a model based on Gaussian process regression (GPR) combined with principal component analysis (PCA) was proposed for the quantitative evaluation of JRC. Notably, eight parameters were selected as indicators for the comprehensive expression of the rock joint roughness. To analyse the model's performance, a publicly available dataset of 112 rock joint profiles was used as an example, of which 95 were chosen as training samples and 17 were chosen as validation samples. The reliability of the model was verified by comparing the predicted results with the measured JRC values. Results The results show that the derived GPR model demonstrates promising performance (R2=0.972, MSE =0.517) for estimation of JRC values, indicating the high applicability of the model in constructing implicit relationships between multiple statistical parameters and JRC values even under small sample conditions. Conclusion In general, the GPR model may provide a new way of estimating JRC values with artificial intelligence.