This chapter explores the transformative impact of artificial intelligence (AI) on the discovery, design, and deployment of advanced polymeric materials in real-world applications. By leveraging machine learning and data-driven approaches, researchers have accelerated the identification of novel polymers with tailored properties, addressing challenges that traditional experimental strategies face in navigating vast chemical design spaces. The chapter examines state-of-the-art AI techniques enabling the rapid prediction of polymer performance, virtual screening, and inverse molecular design, supported by examples in sectors such as energy storage, biomedical devices, sustainable packaging, and environmental remediation. Key considerations, such as the interpretability of machine learning models, the integration of experimental synthesis workflows, and issues of processability and scalability, are addressed to bridge the gap between computational discovery and industrial application. By highlighting recent breakthroughs and ongoing challenges, the chapter underscores AI’s growing role in engineering next-generation polymers that meet sustainability, performance, and cost-effectiveness criteria for diverse technological domains.