This study investigates the prediction of intrinsic solubility through two developed models: a DNN model optimized solely with hyperparameters (HPs) and an ANO model incorporating both feature engineering and HP optimization. The ANO model demonstrates superior performance, with high predictive accuracy across diverse datasets. Additionally, the regression model offers insights into the relationships between specific features and solubility, underscoring the importance of MW, molecular polarizability, refractivity, total polar surface area, and van der Waals surface area. The best results were achieved on the Delaney ESOL dataset using a combination of three fingerprints and 49 molecular descriptors, yielding an R 2 score of 0.991 for predicting solubility in drug-like compounds. A dual-round optimization process was implemented to identify the best models, considering performance and feature count. This study also examined the impact of molecular features on the ML model's predictions through SHAP analysis, uncovering correlations between features and their influence on predicting intrinsic solubility. The integration of the ANO model with feature analysis represents a promising direction for future research, providing insights into the complex interactions between molecular properties and solubility.