This study presents a practical method for estimating the solid-liquid equilibrium (SLE) phase diagrams of deep eutectic solvents (DESs) using machine learning (ML) and quantum chemical (QC) techniques. The approach leverages structural information to predict the melting points and fusion enthalpies of pure compounds, enabling the estimation of SLE phase diagrams for binary mixtures. The ML models, trained on datasets of pure compounds, demonstrated high accuracy in predicting melting points (R² = 0.84; RMSE = 40.53 K) and fusion enthalpies (R² = 0.84; RMSE = 4.96 kJ mol⁻¹). By analyzing the eutectic points in an extensive chemical space, the study highlights the impact of mole fractions and melting properties on eutectic temperatures. Molecular dynamics simulations further emphasize the role of hydrogen bonds in determining mixture behavior. DESs are promising green solvents with potential applications in various industries. However, the lack of accurate phase diagrams has led to misidentification of eutectic points. This study addresses this challenge by providing a method to estimate SLE phase diagrams, which is crucial for understanding DES formation. The integration of ML and QC techniques allows for the prediction of thermodynamic parameters, such as activity coefficients, which are essential for determining the nature of eutectic mixtures. The results show that DESs exhibit negative deviations from ideality, characterized by strong hydrogen bonding interactions between components. The study also demonstrates that the eutectic point coordinates (x_E, T_E) vary depending on the chemical composition, challenging the assumption of fixed molar ratios. The analysis of SLE phase diagrams reveals that many mixtures exhibit either positive or negative deviations from ideality, depending on the interactions between components. The findings underscore the importance of considering hydrogen bonding in the behavior of DESs and highlight the need for accurate phase diagrams to avoid misclassification of eutectic mixtures. The developed method can be applied to a wide range of chemical compounds, facilitating the discovery of new DESs and their potential applications. Future research should focus on improving prediction accuracy through advanced algorithms and experimental validation of the estimated phase diagrams. This study provides a valuable framework for the development of sustainable solvents and contributes to the broader understanding of eutectic mixtures.This study presents a practical method for estimating the solid-liquid equilibrium (SLE) phase diagrams of deep eutectic solvents (DESs) using machine learning (ML) and quantum chemical (QC) techniques. The approach leverages structural information to predict the melting points and fusion enthalpies of pure compounds, enabling the estimation of SLE phase diagrams for binary mixtures. The ML models, trained on datasets of pure compounds, demonstrated high accuracy in predicting melting points (R² = 0.84; RMSE = 40.53 K) and fusion enthalpies (R² = 0.84; RMSE = 4.96 kJ mol⁻¹). By analyzing the eutectic points in an extensive chemical space, the study highlights the impact of mole fractions and melting properties on eutectic temperatures. Molecular dynamics simulations further emphasize the role of hydrogen bonds in determining mixture behavior. DESs are promising green solvents with potential applications in various industries. However, the lack of accurate phase diagrams has led to misidentification of eutectic points. This study addresses this challenge by providing a method to estimate SLE phase diagrams, which is crucial for understanding DES formation. The integration of ML and QC techniques allows for the prediction of thermodynamic parameters, such as activity coefficients, which are essential for determining the nature of eutectic mixtures. The results show that DESs exhibit negative deviations from ideality, characterized by strong hydrogen bonding interactions between components. The study also demonstrates that the eutectic point coordinates (x_E, T_E) vary depending on the chemical composition, challenging the assumption of fixed molar ratios. The analysis of SLE phase diagrams reveals that many mixtures exhibit either positive or negative deviations from ideality, depending on the interactions between components. The findings underscore the importance of considering hydrogen bonding in the behavior of DESs and highlight the need for accurate phase diagrams to avoid misclassification of eutectic mixtures. The developed method can be applied to a wide range of chemical compounds, facilitating the discovery of new DESs and their potential applications. Future research should focus on improving prediction accuracy through advanced algorithms and experimental validation of the estimated phase diagrams. This study provides a valuable framework for the development of sustainable solvents and contributes to the broader understanding of eutectic mixtures.