It is important to predict the unconfined compressive strength (UCS) of stabilized organic soils to be used in designing of foundations. Although machine learning (ML) methods have potential, the literature does not provide overall comparisons. This research suggests a new ML model that uses support vector regression (SVR), multilayer perceptron (MLP), and gradient boosting regression (GBR) and particle swarm optimization (PSO), and hunger games search (HGS) to optimize hyperparameters. The models are trained with 227 measurements of UCS and assessed with the help of cross‐validation. SHapley Additive exPlanations (SHAPs) are used to analyze the importance of the features. The MLP‐HGS and MLP‐PSO model has outstanding performance and the R 2 of 0.999, low root mean square error (RMSE), and mean absolute error (MAE). The most powerful variable that can be identified is cement content, followed by sand, clay, and gravel. The given framework is more accurate and easily interpretable compared to existing studies. The presented innovative method helps to develop ML‐based geotechnical modeling. The results provide information on how the soil stabilization measures can be optimized and the design of stabilized soil foundation made more straightforward.
Utkarsh et al. (2026) studied this question.