This article presents a unique approach employing Least Square Support Vector Regression ( \(\text{LSSVR}\) ) analysis to anticipate the soil-stabilizer compounds’ Unconfined Compressive Strength ( \(\text{UCS}\) ). The study shows a strong relationship between UCS and important intrinsic soil characteristics, such as linear shrinkage, particle size distribution, and the kind and amount of stabilizing additives. Custom \(\text{LSSVR}\) prediction models take a thorough approach to address these factors. Careful considerations are put into creating these models to guarantee accurate UCS estimation. This study combines 2 meta-heuristic algorithms, Sand Cat Swarm Optimization (SCSO) and Coronavirus Herd Immunity Optimizer (CHIO), to increase accuracy. These algorithms examine \(\text{UCS}\) samples from different kinds of soil and contrast the model's predictions with previous stabilization experiment results. 3 distinct models are produced by the study: the LSSC, LSCH, and an independent \(\text{LSSV}\) . All of these models provide valuable insights that aid in precise \(\text{UCS}\) forecasts. Notably, the LSSC model outperforms the others with remarkable statistical measures such as an exceptionally low RMSE value of 90.72 and an amazing \({R}^{2}\) value of 0.995. These results demonstrate the LSSC model's precision, durability, and predictive capacity. This strategy demonstrates a workable method for precise UCS forecasting in a range of engineering applications involving soil-stabilizer blends and highlights the significant improvement attained by adding meta-heuristic algorithms. This improvement results in more accurate forecasts, significantly impacting the construction sector.
