This study presents a practical approach to estimating the solid-liquid equilibrium (SLE) phase diagrams of deep eutectic solvents (DESs) by integrating machine learning (ML) predictions and quantum chemical (QC) calculations. The ML models, including random forest (RF), extreme gradient boosting (XGB), and multilayer perceptron (MLP), were developed to predict the melting points and fusion enthalpies of pure compounds with high accuracy (R² = 0.84; RMSE = 40.53 K and 4.96 kJ/mol, respectively). These predictions were used to estimate the SLE phase diagrams for 3000 binary mixtures of hydrogen-bond acceptors (HBAs) and donors (HBDs). The analysis of eutectic point coordinates revealed that each DES exhibits a distinct eutectic point at a specific composition, diverging from commonly assumed fixed molar ratios. The magnitude of the eutectic temperature (TE) was strongly correlated with the melting properties of HBAs. MD simulations for selected mixtures at eutectic conditions highlighted the importance of hydrogen bond interactions in dictating mixture behavior. The developed approach can be expanded to a vast chemical space, facilitating the development of DESs and accelerating the discovery of greener solvents for industrial applications.This study presents a practical approach to estimating the solid-liquid equilibrium (SLE) phase diagrams of deep eutectic solvents (DESs) by integrating machine learning (ML) predictions and quantum chemical (QC) calculations. The ML models, including random forest (RF), extreme gradient boosting (XGB), and multilayer perceptron (MLP), were developed to predict the melting points and fusion enthalpies of pure compounds with high accuracy (R² = 0.84; RMSE = 40.53 K and 4.96 kJ/mol, respectively). These predictions were used to estimate the SLE phase diagrams for 3000 binary mixtures of hydrogen-bond acceptors (HBAs) and donors (HBDs). The analysis of eutectic point coordinates revealed that each DES exhibits a distinct eutectic point at a specific composition, diverging from commonly assumed fixed molar ratios. The magnitude of the eutectic temperature (TE) was strongly correlated with the melting properties of HBAs. MD simulations for selected mixtures at eutectic conditions highlighted the importance of hydrogen bond interactions in dictating mixture behavior. The developed approach can be expanded to a vast chemical space, facilitating the development of DESs and accelerating the discovery of greener solvents for industrial applications.