Researchers are utilizing machine learning approaches to improve polymer reaction engineering, including predicting monomer reactivity ratios and optimizing polymer production. Various methods are being explored, such as language models, molecular descriptors, and graph kernel methods, to predict properties and performance of polymers. These approaches are being applied to different areas, including the development of sustainable polymers, high-performance batteries, and advanced materials like hydrogels and thermoplastic elastomers.