The design of nucleic acid drugs, including antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and aptamers, has become a promising therapeutic strategy for various diseases. However, the traditional approach to nucleic acid drug design is often time-consuming and resource-intensive, requiring extensive experimentation and trial-and-error processes. Artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering advanced capabilities for accelerating the design and optimization of small nucleic acid drugs. AI techniques, including machine learning, deep learning, and natural language processing, can analyze vast amounts of biological data to predict potential drug candidates, optimize sequences, and enhance binding affinity and specificity. This paper explores the role of AI in the development of small nucleic acid drugs, focusing on AI-driven tools for sequence optimization, property prediction, and in silico screening. The integration of AI into nucleic acid drug design holds the potential to revolutionize the development of next-generation therapies for genetic diseases, cancers, and viral infections, providing faster, more efficient, and cost-effective solutions in the pharmaceutical industry.