Abstract: Drug-target interaction (DTI) is fundamental to novel drug research and development (R&D), playing a critical role in elucidating drug mechanisms of action and improving the cost-effectiveness of drug discovery. Although China has seen rapid accumulation of drug-target resources in recent years, the industry still faces challenges such as a shortage of original targets and excessively high target concentration. The rapid advancement of artificial intelligence (AI) has provided an efficient technological pathway for DTI prediction, establishing it as a core tool for accelerating drug discovery. This paper systematically reviews the fundamental data support framework for DTI prediction tasks, elaborating in detail on three core data types-drug characterization, target characterization, and drug-target associations-as well as the classification and application boundaries of mainstream benchmark datasets tailored to different tasks within the field. On this basis, it comprehensively surveys the technological evolution of AI-driven DTI prediction, summarizing the core paradigms and technical advances of traditional machine learning and deep learning methods in a single-modality setting, alongside cutting-edge multimodal approaches that integrate two modalities (cross-subject/within-subject fusion) and three or more modalities for multidimensional information integration. Finally, this paper analyzes the current major challenges in data quality, model generalizability and interpretability, and real-world deployment, while also discussing future trends in standardized dataset construction, generative AI, and large-scale biomedical foundation models, aiming to provide a reference for both research and industrial applications in the AI-driven DTI prediction field.