The aim of this study has been to develop machine learning (ML)-based solubility models as a viable alternative to quantum computational methods. To achieve this, we undertook a systematic process that included data cleaning and analysis, descriptor development, and outlier detection. We proposed a workflow to refine experimental data, focusing on producing useful models through effective outlier detection.