Abstract: Global data is undergoing explosive growth, and traditional storage solutions, represented by silicon-based media, are facing significant challenges in terms of physical limits and sustainability. Hence, non-silicon-based biological storage technologies are gradually emerging, with DNA-based data storage pathways developing most rapidly and regarded as a highly promising nextgeneration storage solution. DNA data storage technology utilizes artificially synthesized deoxyribonucleic acid (DNA) molecules as information carriers, offering revolutionary advantages such as ultra-high storage density, millennial-scale stability, and near-zero maintenance energy consumption. However, its path to practical application remains constrained by multiple bottlenecks, including coding efficiency, error rates and control costs, and data retrieval speed. In recent years, the rapid advancement of artificial intelligence (AI) technology has injected new momentum into overcoming these challenges. This paper briefly introduces the principles and challenges of DNA storage, reviews key applications of AI in DNA storage, with a focus on its latest progress in encoding optimization, error correction, and efficient retrieval, and discusses current technological limitations as well as future development directions.