This study proposes a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS), enhanced with Particle Swarm Optimization (PSO), to streamline the assessment of pile foundation bearing capacity. Traditional load testing methods are expensive and lengthy, often limiting real-world applicability. Leveraging 257 pile load test samples—which include parameters such as breadth (mm), drop height (m), ram weight (kN), and pile length (m)—this model aims to predict ultimate pile capacity (kN) more accurately and efficiently. Key hyperparameters for the ANFIS-PSO model were optimized, including a population size of 20, learning coefficients of 0.2 (individual) and 0.3 (global), and a maximum of 1000 iterations. During testing, the hybrid model demonstrated robust predictive accuracy with a R2 value of 0.85 and a Root Mean Squared Error (RMSE) of 0.09, markedly outperforming the standalone ANFIS model, which had a R2 of 0.82 and RMSE of 0.107. The model’s reliability was confirmed through Taylor diagrams and statistical validation metrics, including a Mean Absolute Error (MAE) of 0.071 for the ANFIS-PSO, compared to 0.082 for ANFIS alone. Besides increasing the accuracy of forecast, this hybrid method offers a scalable and efficient pile capacity assessment technique for numerous applications in engineering that would save enormous amounts of time and money usually consumed in conventional testing.