The focus of this paper is a strategy for making a prediction at a point where a function cannot be evaluated. The key idea is to take advantage of the fact that prediction is needed at one point and not in the entire domain. This paper explores the possibility of predicting a multidimensional function using multiple one-dimensional lines converging on the inaccessible point. The multidimensional approximation is thus transformed into several one-dimensional approximations, which provide multiple estimates at the inaccessible point. The Kriging model is adopted in this paper for the one-dimensional approximation, estimating not only the function value but also the uncertainty of the estimate at the inaccessible point. Bayesian inference is then used to combine multiple predictions along lines. We evaluated the numerical performance of the proposed approach using eight-dimensional and 100-dimensional functions in order to illustrate the usefulness of the method for mitigating the curse of dimensionality in surrogate-based predictions. Finally, we applied the method of converging lines to approximate a two-dimensional drag coefficient function. The method of converging lines proved to be more accurate, robust, and reliable than a multidimensional Kriging surrogate for single-point prediction.