Predictive modeling is an important tool that is significantly transforming the field of polymer science. It has resulted in better understanding, design, and optimizations of polymeric materials as per requirements. Predictive models, trained on different experimental data sets, can accurately predict structure–property relations. In addition to this, predictive modeling also helps in the selection of promising materials by forecasting desirable properties of polymers. The application of predictive modeling for polymers highlights the significance of polymers in modern society and emphasizes evolving paradigms in materials research. It focuses on the emergence of a data-driven approach for polymer design, in turn integrating machine learning algorithms and big data analytics to accentuate the discovery of new functional polymers. The factors such as data availability, data quality, the complex nature of polymer systems, interpretability, and integration with existing tools and processes, pose huge challenges and are discussed in this current chapter. The machine learning applications include materials informatics, property prediction, design, and identification of novel polymers. In order to find new materials and information more quickly for the next research initiatives, the discipline of polymer informatics has enormous promise for both the adoption and advancement of machine learning as well as the search for new polymers.