This study explores the use of Steel Slag Aggregate (SSA) as a sustainable alternative in concrete, addressing environmental concerns like resource depletion and high CO2 emissions. Four modeling techniques—Gene Expression Programming (GEP), Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—were used to predict the compressive strength (CS) of SSA concrete using 367 datasets. Among the models, Gradient Boosting (GB) showed the best performance, with the highest R2 values and lowest error metrics, outperforming RFR, GEP, and ANN. The findings highlight GB's effectiveness in predictive modeling for sustainable construction.