Various methods are employed to ascertain the deformation modulus of rock masses, encompassing indoor and outdoor testing, numerical analysis, inverse analysis, and rock mass classification. However, these methods are characterized by notable limitations. Advancements in neural network techniques offer a promising avenue for predicting rock mass parameters through modeling. Utilizing a dataset comprising 88 sets obtained from literature, this study employed rock quality indicators including Rock Quality Designation (RQD), Rock Mass Rating (RMR), and compressional wave velocity (Vp). Leveraging these factors, an enhanced Radial Basis Function (RBF) neural network model was developed, incorporating the pattern search method to predict the deformation modulus of rock masses. To assess the model's accuracy, 17 datasets from the rock masses of the literature taken as test set, and the resultant predictions were compared with those derived from a Backpropagation (BP) neural network model and in-situ data. The findings indicate that the enhanced RBF model exhibits superior performance in predicting the deformation modulus of hard rock masses.