Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost) model, optimized using a hybrid particle swarm optimization and genetic algorithm (PSO–GA), was developed. This hybrid model estimates the SWR across a broad suction range, accounting for both drying and wetting paths, along with key soil parameters. The performance of the model was evaluated through various statistical analyses and by comparing the predicted gravimetric water content with experimental data. A backward feature elimination method was employed to assess the impact of various input parameters on model accuracy and to offer a simplified model for scenarios with limited data availability. Additionally, Monte Carlo simulations were conducted to quantify the inherent uncertainties associated with the dataset, XGBoost hyperparameters, and model performance. The hybrid PSO–GA XGBoost model effectively estimates the water retention of clayey soils during both drying and wetting cycles, proving to be an alternative to traditional soil mechanics correlations. • PSO–GA XGBoost predicts soil–water retention with high accuracy across a wide suction range. • Model captures clay soil’s drying–wetting paths and quantifies uncertainties through Monte Carlo. • Reduced feature models maintain reliability, balancing simplicity and precision in applications.