The separation of azeotropic mixtures, such as methyl acetate and methanol remain a major challenge in chemical process 

design due to strong molecular interactions and low relative volatilities. In this study, a green and efficient deep eutectic 

solvent (DES) based on choline chloride (ChCl) and ethylene glycol (EG) (1:3) was introduced as a sustainable entrainer 

to disrupt the azeotrope at 323.15 K. Vapor–liquid equilibrium (VLE) and vapor–liquid–liquid equilibrium (VLLE) experiments

revealed complete azeotrope elimination at only 0.4 wt% DES loading, confirming its superior selectivity and 

phase-splitting capability. Beyond the experimental findings, a Physics-Guided Artificial Neural Network–Gaussian Process

Regression (PI-ANN+GPR) hybrid framework was developed to predict key thermodynamic properties. The hybrid 

model outperformed conventional regression methods by achieving near-perfect parity and statistically calibrated uncertainty

bounds (PICP₉₀ ≈ 0.90). This integrated experimental–computational approach not only validates ChCl: EG as a 

viable green entrainer but also demonstrates a scalable machine learning route for data-efficient, uncertainty-aware design 

of DES-assisted separations, establishing a new benchmark for reliability-driven solvent screening.