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In this study, a neural-fuzzy (NF) system is combined with group method of data handling (GMDH) in order to estimate the axial bearing capacity of driven piles. To reach optimum design of this conjunction (NF-GMDH) network, the metaheuristic techniques including particle swarm optimization (PSO) and gravitational search algorithm (GSA) were utilized. The datasets used for estimating pile bearing capacity were collected from the literature review. The parameters influencing the modeling and pile capacity analysis were taken into account as Flap number, surrounding soil properties, the pile geometric characteristics, and internal friction angles of the pile–soil interface. The efficiency of hybrid NF-GMDH networks in train and test phases was examined. Applying the PSO algorithm to the hybrid NF-GMDH model structure improved the model performance and achieved a higher level of accuracy in predicting the ultimate pile bearing capacity (RMSE = 1375 and SI = 0.255) compared to NF-GMDH model developed by GSA (RMSE = 1740.7 and SI = 0.357). In addition, based on achieved results, the developed NF-GMDH networks showed relatively better performances in comparison with gene programming and linear regression model methods considered in this study.