Organic synthesis plays a crucial role in drug discovery, polymer synthesis, material science, agrochemicals, and specialty chemicals. Their synthesis and process optimization require substantial resources and are labour-intensive, often performed by exploring a single variable in search of optimal conditions while disregarding the intricate interactions among competing variables within the synthesis process. The complexity of the problem highly increases considering that process optimization often requires solutions that meet multiple targets, such as yield, selectivity, purity, cost, environmental impact, etc. In recent years, the advancement of artificial intelligence (AI), machine learning (ML) and automation has produced a shift in paradigm for chemical synthesis optimization techniques. By leveraging on ML models to predict reaction outcomes and ML optimization algorithms, this new framework has demonstrated the ability to navigate the complex relationships between reaction variables and to find global optimal conditions in fewer number of experiments than traditional methods . In addition, machine-guided multi-objective optimization (MOO) has emerged as a promising framework to obtain reaction conditions that perform optimally for multiple target objectives, enabling researchers to explore diverse solution spaces and uncover optimal conditions that strike a balance between consonant and/or conflicting targets. In addition, the incorporation of lab robotics into chemical synthesis has enabled the development of closed-loop optimization platforms capable of executing optimization campaigns rapidly with minimal human intervention, relieving experimenters from labour-intensive tasks and reducing the overall process development lead time .