Historically, optimization of chemical reactions has been performed using methodologies derived from the design of experiments (DOE) with the objective of maximizing the yield of the reaction product. However, these techniques are not wellsuited to find the global optimal conditions and scale exponentially with the number of variables. Computational approaches that rely on optimization algorithms offer more efficient methods to obtain optimal conditions without requiring an exponential number of experiments per optimization variable. Early examples of optimization of chemical reaction conditions through computational approaches focused on the application in "black-box" optimization algorithms such as steepest descent, SNOBFIT and Nelder-Mead Simplex demonstrated positive results and the ability to perform self-optimizing automated workflows with little human intervention . In recent years, the incorporation of ML optimization methods has demonstrated the ability to obtain optimal reaction conditions in a reduced number of experiments in comparison to human intuition, traditional DOE and other "black-box" optimization algorithms . Opposed to previous examples of optimization algorithms, the ML approach is characterized by the construction of predictive models that map relationships between the reaction conditions and the target optimization objectives. In this section we review the latest developments of ML optimization strategies for the optimization of chemical reactions.