This study examines the construction of generative AI training datasets for the digital assetization and industrial application of traditional Korean patterns within the contemporary era of hyper-scale artificial intelligence (AI). Utilizing the 2024 “Korean Traditional Pattern Data Establishment Project” by the Korea Heritage Agency as an exploratory case study, this research analyzes the end-to-end lifecycle of data development, including collection, refinement, processing, and quality control. By structuralizing these empirical outcomes, the study proposes concrete methodologies and versatile application frameworks for the digital cultural content industry. Specifically, the research evaluates classification systems categorized by morphology, historical utility, and era, alongside the implementation of bilingual (Korean-English) image captioning techniques for multimodal AI training. Furthermore, it explores the integration of these datasets into emerging sectors such as the metaverse, digital art, and Extended Reality (AR/VR/XR), while outlining strategic pathways for public data dissemination and the cultivation of a sustainable data ecosystem. This analysis provides an empirical foundation for promoting the digital revitalization of traditional cultural heritage while ensuring the preservation of its cultural identity and aesthetic authenticity.