One of the essential parameters in Grouting operations for various purposes of foundation improvement is the optimal Grouting pressure for the penetration of cement slurry. In this study, the role of effective parameters in determining the optimal Grouting pressure was investigated based on Grouting data and engineering geology data obtained in drilling and Grouting workshops from 42 dam projects in Iran and the world. Then, Multiple Linear Regression (MLR) and Multiple Nonlinear Regression (MNLR) methods and soft computation methods such as Fuzzy System (FUZZY), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to estimate optimal Grouting pressure from these parameters. The results show that the depth (D), geological strength index (GSI), and uniaxial compressive strength of rock mass (UCSRM) have the highest correlation in the Grouting pressure (GP), respectively. Among the statistical methods, the ANFIS method with a coefficient of determination (R2 = 0.803) and the root mean square error (RMSE = 4.47) performs better than other models.Additionally, the results show that R2 and RMSE are improved in FUZZY, ANN, and ANFIS analysis methods compared to MLR and MNLR. In the fuzzy system, fuzzy rules are formulated using the worker's experience as well as the results of studies of others. These rules select the output using specific ranges of input values, so fuzzy systems models show more flexibility and give better results than the data used in the model.