Soil stabilization with industrial by-products is increasingly recognized as a cost-effective, low-carbon alternative to conventional chemical binders in developing-country infrastructure. This study proposes an integrated industrial engineering and operations management (IE/OM) framework that combines Response Surface Methodology (RSM), Monte Carlo simulation, and the Analytic Hierarchy Process (AHP) to evaluate Electric Arc Furnace steel slag (SS) and sawdust ash (SDA) for improving lateritic subgrade soils in Osun State, Nigeria. Second-order RSM models were developed for soaked California Bearing Ratio (CBR), Maximum Dry Density (MDD), and Optimum Moisture Content (OMC) across nine mix configurations, achieving high in-sample fit (R² = 0.996–0.997), indicative of parametric efficiency at small sample size (n = 9). Leave-One-Out Cross-Validation yielded an indicative Q² = 0.956 (RMSE = 2.55 pp), though further validation is required. Optimization identified a provisional optimum at 20% SS and 6.6% SDA (predicted CBR = 58.0%). Monte Carlo simulation incorporating dosing variability and model residuals produced an indicative process capability (Cpk = 1.50). Complementarily, AHP—using literature-informed stakeholder weights—ranked 15% SS and 7% SDA as the preferred blend (score = 0.848). The framework is therefore presented as a transferable proof-of-concept decision-support tool, with its quantitative outputs interpreted as indicative pending validation through larger replicated designs, soil-specific modelling, stakeholder elicitation, and field-scale trials.