In this work, we developed an ML workflow for organic co-solvent identification. Developing the ML workflow involved creating two ML models, one for each of the two solubility datasets: AqSolDB (aqueous solubility) and BigSolDB (organic solubility). We demonstrated the strong cross-validation and testing performance of both solubility models. The generalizability of the organic solubility model was further assessed on unseen solutes. We showed that, while the model struggles with quantitative solubility predictions, it can predict qualitative solubility trends. As such, the organic solubility model can distinguish between good and poor organic solvents. We showed the utility of using this workflow pipeline for identifying water-miscible organic solvents, which is a crucial property of an organic co-solvent. The effectiveness of our approach in identifying suitable organic co-solvents was further validated through experimental evaluation involving five different organic solvents on two molecules of interest-limonene and benzaldehyde. The study demonstrates that the ML workflow successfully predicted several cosolvents, highlighting its potential for effective co-solvent identification. However, the study also reveals the limitations of our approach, which does not account for the non-idealities of mixing on solubility. We also note that while the organic solubility model can distinguish between good and poor solvents, it may still struggle with precise ranking among the good solvents. We anticipate that the development of larger, more balanced solubility datasets will improve the accuracy of the organic solubility model, further solidifying its utility.