A standard workflow and general methodology for organic reaction optimization through ML methods is shown in Figure . The workflow comprises (i) careful design of experiments; (ii) reaction execution with commercial high-throughput systems or inhouse designed reaction modules; (iii) data collection by inline/offline analytical tools; (iv) applying optimization algorithms on collected data points to map with the target objectives; (v) prediction of optimal solutions and (vi) experimental testing of suggested optimization results. Through an examination of methodologies, algorithms, and various case studies, the article offers our perspective into the state-of-the-art techniques for optimizing synthesis of organic molecules highlighting both challenges and prospects. The structure of this perspective follows the steps presented in Figure where in Section 2 we review the high-throughput platforms currently used to perform chemical reaction optimization. Section 3 discusses the techniques and developments on analytical tools and data processing algorithms. Section 4, discusses the latest trends in the selection of optimization algorithms for chemical synthesis. Finally, Section 5 we highlight the future directions and opportunities in the field. For an in-depth review on the topics of chemical reaction optimization, the readers can gain diverse insights from prominent reviews by Taylor et al. , Griffin et al. and Sagmeister et al. . The first two offer valuable perspectives on chemical reaction optimization, particularly focusing towards process scale-up, while the latter discusses the potential of flow platforms for conducting self-optimization reactions. Additionally, we refer the readers to the following literature in other areas relevant to the application of ML to chemical synthesis that is not covered by this perspective, such as small molecule discovery , drug discovery , retrosynthesis , and catalyst selection/design .