The development of 5G, artificial intelligence and new energy technologies imposes stringent requirements on thermal management, driving the need for polymers with high intrinsic thermal conductivity (TC). In this review, we summarize recent progress in this field. We begin by examining heat transfer mechanisms in polymers, from atomic-scale phonon transport to the collective effects of chain conformation, alignment, and morphology. We then analyze how key structural factors—notably intrachain and inter-chain interactions—govern thermal transport. Furthermore, we review experimental strategies to enhance TC, with a focus on improving chain alignment and molecular design. The complementary role of computational methods, such as first-principles calculations and molecular dynamics simulations, in revealing microscopic transport mechanisms and predicting thermal properties. We further highlight the growing influence of machine learning, which accelerates the mapping of structure- property relationships, enables high-throughput prediction of thermal conductivity, and supports the inverse design of new polymers. Finally, we identify prevailing challenges concerning fundamental mechanistic understanding, the scalability of fabrication techniques, and long-term performance stability, while suggesting promising research directions for the rational design and practical implementation of next-generation, highTC polymers in advanced thermal management.