In this article, we outlined the latest advances in machine learning-driven multiobjective optimization for chemical synthesis, in addition to breakthroughs in high throughput experimentation and analytical techniques. The recent developments of ML algorithms, high throughput experimentation tools, data processing techniques, and self-optimizing reactors has been a transformative force for chemical optimization processes. However, there are still plenty of research opportunities to continue the transformation of the field and to accelerate the execution of chemical reaction optimization. Recent work has had substantial progress in optimizing multiple continuous variables, yet the utilization of categorical variables in chemical synthesis optimization has predominantly been confined to single-step reactions with one or two optimization objectives. The development of ML algorithms that can efficiently optimize a larger number of categorical variables will be crucial to unlocking the full potential of optimization methods. This is particularly true when objective functions that go beyond direct measurements of the reaction product outputs (e.g. yield, throughput, selectivity, etc.) are targeted. For example, optimizations that target to minimize the environmental impact of chemical synthesis is becoming a priority in the pharmaceutical industry. The environmental impact of a reaction not only depends on the efficiency of the process (i.e. yield and throughput) but will be highly affected by the nature of the solvent, catalyst, choice of reagents, downstream workup, etc. used during synthesis. To obtain optimal reaction conditions that minimize the environmental impact, the exploration of a large number of different reagents may be required, which is not possible to perform through traditional optimization methodologies. Nonetheless, ML algorithms could offer an efficient approach to navigate the parametric space and to reduce the experimentation time to find the conditions that minimize environmental impact of a particular manufacturing process. However, the state-of-the-art optimization algorithms still fall off from the target.