<p>Metal complexes constitute a key component of modern chemistry due to their structural diversity, adjustable electronic properties, and broad applicability in catalysis, materials science, and biomedicine. In recent years, artificial intelligence (AI) has increasingly contributed to accelerating the discovery and development of metal complexes by supporting several stages of research, including molecular design, synthesis, characterization, and functional optimization. These advances have improved the efficiency of discovery processes while also enhancing sustainability and the reliability of predictive models. This review first considers traditional coordination chemistry approaches alongside recently developed environmentally sustainable methods for the synthesis of metal complexes. Particular attention is given to ongoing challenges associated with reaction optimization, scalability, and reproducibility. In addition to experimental methodologies, machine learning methods are increasingly employed to complement conventional strategies by enabling a rapid estimate of physicochemical properties and catalytic activity. In the biomedical field, particular focus is placed on platinum- and ruthenium-based anticancer complexes. In this area, AI-assisted drug discovery strategies, computational molecular design, and predictive modeling have supported the development of next-generation metal-based therapeutics characterized by improved selectivity and reduced toxicity. The review also examines catalytic applications of metal complexes, including cross-coupling reactions, hydrogenation, transfer hydrogenation, electrocatalysis, photoredox catalysis, carbon dioxide activation, and asymmetric catalysis. Recent developments in hybrid photoelectrodes, supported catalytic systems, redox-active ligands, and rational molecular catalyst design are also considered. Furthermore, emerging AI-driven approaches for catalyst discovery, such as generative models, inverse design strategies, closed-loop automated experimentation, and high-throughput virtual screening, are presented as effective tools for accelerating catalyst development. Finally, the review discusses current limitations associated with AI-based methodologies, particularly those related to data availability, model interpretability, and generalizability. Future directions emphasize the importance of integrating explainable AI with mechanistic understanding in coordination chemistry in order to improve the reliability and interpretability of catalyst and drug discovery processes.</p>