Environmental DNA (eDNA) metabarcoding has revolutionized biodiversity monitoring by offering a non-invasive, scalable alternative to traditional sampling methods. However, the exponential growth of eDNA metabarcoding research has made it difficult to stay current with the latest advancements in the field. By leveraging AI-driven large language models (LLMs) through a custom GPT pipeline, we automated the rapid aggregation and synthesis of data from 1,428 DNA metabarcoding studies spanning an array of terrestrial, freshwater, and marine environments. This dataset serves as a case study to explore two key questions in field of eDNA metabarcoding: (1) how sequencing depth influences biodiversity detection, and (2) how DNA metabarcoding compares to traditional biodiversity assessment methods. The studies were systematically categorized based on ecosystem type, taxonomic targets, and methodological variables, enabling comprehensive evaluation of key questions and broader trends in eDNA research. These results provide researchers and practitioners with actionable insights to refine sampling designs, optimize sequencing protocols, and guide future research priorities in the field of eDNA metabarcoding. Comparisons between eDNA metabarcoding and traditional methods, such as seining and visual surveys, will reveal advantages in certain contexts, including increased detection sensitivity and reduced environmental impact. However, inconsistences across studies underscore the need for methodological standardization. In addition to insights gained on standardization and biodiversity assessment methods, our analysis will uncover gaps in geographic and ecosystem representation, emphasizing the need for broader applications in underrepresented regions and habitats. This pipeline demonstrates how integrating rapidly evolving generative AI tools with traditional synthesis can quickly and efficiently deepen our knowledge in other continuously advancing fields of research, like metabarcoding. In doing so, this meta-analysis offers a comprehensive overview of eDNA metabarcoding's strengths, limitations, and future potential in biodiversity monitoring.