Enthalpy is a key thermodynamic parameter governing energy extraction efficiency in Enhanced Geothermal Systems (EGS). Although Machine Learning (ML) has been widely applied in geothermal modeling, few studies have systematically integrated Response Surface Methodology (RSM) with ML to develop and compare multiple predictive models for enthalpy production. Using datasets from CMG STARS simulations, we developed predictive models based on RSM and four ML techniques (Random Forest, Decision Tree, XGBoost, and Support Vector Machine). A Central Composite Design (CCD) in CMG CMOST established relationships between operational parameters and enthalpy, while Particle Swarm Optimization (PSO) deter... More >