Herein, we develop an ML workflow for identifying organic co-solvents for organic molecules of interest. We initially evaluate two model architectures: Light Gradient Boosting Machine (LGBM) and Random Forest (RF) (Fig. ). Additionally, we examine both of these model architectures using three different sets of features: Molecular ACCess System (MACCS), group contribution (GC), and a combination of both (GC-MACCS) as schematically represented in Fig. . We evaluate the performance of both model architectures along with the three sets of features for aqueous and organic solubility. We find that the LGBM model trained on GC-MACCS features is best for both cases of solubility predictions. The workflow involves creating two machine learning models, as shown in Fig. -one for aqueous solubility trained on the AqSolDB dataset and the other for organic solubility trained on the BigSolDB dataset. The organic cosolvents are identified via a two-step process (Fig. ) with these two models. The first step involves using the aqueous solubility model to predict the miscibility of solvents in water. This way, solvents that are immiscible in water are removed. This step is independent of the molecule of interest. Having obtained a pool of water-miscible solvents, we assume that solvents can be ranked from best to worst based on the predicted solubility of molecules of interest in them. As such, the second step of the workflow involves the organic solubility model (Fig. ).