<p>The estimation of the thermophysical properties of binary mixtures is important for designing separation processes, solvent systems, and energy-efficient formulations. The work presented in this study focuses on 1-alkanol–ethylene glycol diethyl ether (EGDEE) mixtures, which are widely used as fuel additives and solvents. An artificial neural network (ANN) model was developed to simultaneously estimate four significant properties: density, absolute viscosity, viscosity deviation, and excess molar volume. Among the 3224 molecular descriptors, the molecular mass and overall atomic polarizability were selected as input features, along with the EGDEE mole fraction and temperature. The optimal ANN design with 14 hidden neurons performed well in terms of prediction accuracy, with mean square errors (MSE) of 0.000015, 0.00097, 0.0019, and 0.00029% and determination coefficients (R²) of 0.9993, 1.0000, 0.9983, and 0.9997 for density, absolute viscosity, excess molar volume, and viscosity deviation, respectively. Internal and external validation, Y-randomization, and applicability domain analyses confirmed the robustness and reliability of the model. Sensitivity analysis revealed that molecular mass had the greatest influence on all four properties, with relative importance values of 33.15% for density, 35.30% for absolute viscosity, 31.47% for viscosity deviation, and 32.29% for excess molar volume. To benchmark the ANN model, it was compared with three established approaches: PC-SAFT equation of state for density, PC-SAFT combined with free volume theory for absolute viscosity, and Redlich–Kister equation for excess molar volume and viscosity deviation. The ANN model outperformed all three models, offering superior accuracy and simultaneous multi-property predictions across varying temperatures. These results demonstrate that the proposed ANN model is a powerful and efficient tool for forecasting the mixture properties.</p>