Abstarct: Soil hydraulic parameters are one of the most important features that knowledge of it plays an important role in soil water management. The purpose of this study is estimate soil hydraulic parameters and simulate infiltration. To do this, sampling was performed from two clay loam and silty loam textures. For each texture, three soil types were identified with the percentage of sand, clay and silt. To estimate the hydraulic parameters of the soil, HYDRUS-1D software and Van Genochten-Mualem model and inverse solution method were used. For the initial estimation of soil hydraulic parameters, Rosseta software, which is contained in HYDRUS software, was used. In this software, percentages of clay, silt and sand were introduced as input data and the software estimated the amount of parameters using artificial neural network. Optimal values of these parameters were obtained after several replications. The results of the model in estimating hydraulic parameters showed that in clay loam texture the values of soil hydraulic parameters in soil a and b reached optimal after 4 repetitions and for soil b the optimal values were obtained in the ninth iteration. In loam silt soil, for soil hydraulic parameters, the model used more iterations to obtain optimal values for the parameters. In soil f, the model reached the optimal value for the parameters after 14 replications. While in soil d and e, after 9 and 5 replications, the optimal answer was reached. Cumulative infiltration data obtained from the dual cylinder were considered as observational data. RMSE, MAE, ME and R2 statistics were used to evaluate the model. The results of cumulative infiltration simulation showed that for both groups of soil texture, the model had an acceptable performance in the simulation. In loam clay soil, the model estimated the simulation for soil b (soil with 28% clay, 42% silt and 30% sand) with the lowest error (RMSE = 0.011). While in soil with silty loam texture, and in soil e (soil with 20% clay, 58% silt and 22% sand) the minimum MAE value was calculated to be 0.22-2. Also for this soil the value of RMSE = 0.00028 was obtained which had the lowest error. Also, the values of the Coefficient of Determination and the diagram of the simulated values and observations showed that there is a very good accommodation between the two data groups.