The application of ML algorithms to aid the discovery of new chemistry knowledge is flourishing, from generative design to property prediction and reaction planning. Further work should incorporate the diverse applications of ML in chemistry into chemical reaction optimization campaigns to open new avenues of research and discoveries. In particular, ML tools have shown to have a large potential in the planning of reaction optimization campaigns to assist in the selection of categorical chemical variables (e.g. catalysts, ligands, additives, etc.). Reviewed work by Christensen et al. have already demonstrated the advantages of applying ML clustering methods to discover new catalyst ligands that would have been missed if the selection of test ligands only relied on human chemical insights. Taylor et al. also highlighted the use of DFT or ML alternatives to find similarities between reaction models to apply efficient multi-task learning to chemical reaction optimization. Another potential application of ML tools is the use of CASP to discover alternative reaction routes with the potential to improve the efficiency of current manufacturing methods. Finally, leveraging on the large quantities of data generated from self-optimizing chemical platforms and their experimental versatility, we envision the incorporation of reaction optimization methods with generative design to create full-driving laboratories that both tackle the discovery of new molecules and the search for optimal synthesis conditions to satisfy the production constraints for a chemical commodity.