While more advanced models such as multi-layer perceptrons (MLP) and deep neural networks (DNN) offer improved flexibility and performance by learning complex, non-linear relationships between molecular features, they still face significant challenges. These include the need for extensive manual tuning of numerous hyperparameters, such as learning rates, dropout rates, and layer sizes, which can drastically affect model performance. Furthermore, the manual selection of optimal architectures and hyperparameters often leads to timeconsuming trial-and-error processes, making it difficult to consistently achieve high predictive accuracy across diverse datasets. In this study, we propose a physicochemical deep learning model that utilizes an automated network optimizer (ANO) to improve the prediction of intrinsic solubility for drug-like compounds. Our model employs Bayesian optimization (BO) with tree-structured Parzen estimators (TPEs) to efficiently explore the hyperparameter space, identifying the optimal model configuration. By integrating a DNN model with the advanced HPO capabilities of the autoML method , we enable the simultaneous optimization of molecular features and model parameters from both chemical and machine learning perspectives. This dual optimization strategy aims to not only enhance predictive accuracy and performance but also reduce the time and cost required to achieve optimal solutions . Our approach achieved high performance, with an R 2 = 0.991, surpassing the results obtained using traditional standalone methods such as ridge regression (RR), support vector machine (SVM), and multilayer perceptron (MLP). Additionally, through an analysis of feature importance using the Shapley Additive exPlanations (SHAP) method and a correlation heatmap, we identified key molecular descriptors that significantly influence solubility are molecular weight (Mol_Weight), octanol/water partition coefficient (Mol_logP), total polar surface area (Mol_TPSA), polarizability (Mol_MR), van der Waals surface area, and topological atomic connectivity. Understanding the role of these descriptors not only boosts model performance but also provides valuable insights into the molecular determinants of solubility. These findings demonstrate that the proposed ML method significantly enhances the accuracy of predictive modeling for physical properties. Moreover, it facilitates automated modeling and feature selection for new chemical data, offering a clearer understanding of the principles underlying solubility predictions. This advancement holds the potential to drive the discovery of new materials and improve drug development processes.