Abstract: Ribonucleic acid (RNA) plays a central role in the regulation of biological processes, and its systematic investigation is essential for understanding disease mechanisms at molecular level and developing innovative therapeutic strategies. Nevertheless, progress in RNA research has been hindered by the structural and functional complexity of RNA molecules, the explosive growth of high-throughput sequencing data, and the intrinsic limitations of conventional experimental techniques. In recent years, artificial intelligence (AI) has emerged as a powerful enabling technology, offering novel solutions through its strengths in pattern recognition, high-dimensional data analysis, and predictive modeling. This review provides a comprehensive overview on the major applications of AI in RNA research, encompassing RNA sequence element optimization, RNA modification sites prediction, RNA secondary/tertiary structures prediction, and computational modeling of RNA–protein interactions. Furthermore, the key challenges facing AI-driven RNA studies are critically examined, including the limited availability of high-quality annotated datasets and the insufficient interpretability of complex learning models. Finally, future perspectives are outlined, emphasizing integrated data resource development, algorithmic and model-level innovations, and the construction of closed-loop translational frameworks, with the goal of fostering deeper integration between AI methodologies and RNA biology, accelerating both fundamental discoveries and the development of next-generation RNA-based therapeutics.