Organic Rankine Cycle (ORC) turbines for combined cycle engines offer significant potential for improving fuel efficiency. However, turbine weight remains a critical design factor, as the turbogenerator can account for up to one-third of the power unit mass. The turbine design tool TurboSim, developed by Majer and Pini, is computationally expensive. This study aims to develop an accurate and computationally efficient symbolic regression surrogate model to predict ORC radial-inflow turbine efficiency and weight. While a single multi-output model was initially explored, separate models for each working fluid provided better accuracy. Four fluid categories—refrigerants (R134a), hydrocarbons (butane, cyclopentane, toluene), alcohols (ethanol), and siloxanes (MM)—were analyzed, showing that working fluid has a minor effect on efficiency but a significant impact on turbine weight. A parametric study identified key design variables, with impeller radius ratio and hub-to-tip radius ratio having the greatest impact. While efficiency predictions were generally reliable, weight predictions exhibited greater variability, with lower R² values and higher MSE for some fluids. The results highlight the potential of symbolic regression for rapid and interpretable turbine design optimization. However, further research is needed to improve weight prediction consistency.