Despite the structural complexity of transition metal complexes, ranging from varied oxidation, spin states to the interplay between metal and ligands, notable advancements have been achieved in exploring this domain. Traditional physics-based knowledge approaches capably enumerate novel complexes by re-assembling discrete ligands extracted from available databases. However, the lack of widespread agreement on enumeration protocols results in a poor understanding of practical solutions under various conditions. The emergence of ML drives a paradigmatic transformation in this field. The ML-assisted strategies, e.g. generative models enrich the diversity of generated complexes with the aim of generating novel ligands from scratch without any prior knowledge. In addition, the ongoing development of MLPs allows for efficient screening of generated candidates for tailored properties. More importantly, the remarkable performance of ML models makes it accessible to run simulations for large systems, like zinc-metalloproteins, 172 for which a force field or a DFT method could not achieve both efficiency and effectiveness simultaneously. It is acknowledged that ML provides numerous opportunities to explore the unknown domain in chemistry and is becoming increasingly important for the computational discovery of new materials. Despite the unprecedented progress of ML, challenges remain in maximizing the potentials of ML for the automated workflow for transition metal materials design.