The design and optimization of polymer materials have traditionally relied on empirical and trial-and-error approaches, which are increasingly inadequate for navigating the vast chemical design space of macromolecules. This chapter presents a comprehensive overview of emerging artificial intelligence (AI)-driven strategies for polymer structure optimization, highlighting a paradigm shift toward data-driven and inverse design methodologies. Key aspects include advanced feature representation techniques for polymers, spanning molecular to mesoscale descriptors, and the integration of modern machine learning frameworks such as artificial neural networks, convolutional neural networks, graph neural networks, and transformer-based models for accurate property prediction. The chapter further explores generative modeling approaches—including variational autoencoders, generative adversarial networks, and diffusion models—for the inverse design of novel polymer structures with targeted functionalities. In addition, optimization algorithms such as Bayesian optimization, genetic algorithms, and particle swarm optimization are discussed for efficient exploration of high-dimensional design spaces and experimental planning. A detailed case study on AIoptimized conductive polymers demonstrates how closed-loop, self-driving laboratories enable autonomous discovery and process optimization, bridging the gap between computational design and real-world manufacturing. Finally, the chapter outlines key challenges, including data scarcity, model interpretability, and integration with experimental workflows, and discusses future directions toward human-in-the-loop systems and industrial-scale deployment. Overall, this work provides a unified framework for leveraging AI to accelerate polymer discovery, design, and commercialization.