The convergence of machine learning methodologies with metagenomic analysis has emerged as a transformative paradigm in microbial ecology research, fundamentally revolutionizing understanding about complex microbial communities. The current study examines the integration of artificial intelligence techniques with metagenomic approaches, highlighting their synergistic potential in advancing biological discovery. Through bibliometric analysis from the SCOPUS database (2000–2025), we present a quantitative assessment of research trends, collaboration networks, and scholarly impact in this rapidly evolving interdisciplinary field. Exponential growth in publications since the field’s inception in 2008 was observed along with the broad interdisciplinary applications of these methodologies. Key applications include enhanced taxonomic classification, improved phenotype prediction for disease association studies, and advanced functional annotation capabilities enabling the discovery of novel genes and metabolic pathways. The integration demonstrates superior pattern recognition capabilities in high-dimensional metagenomic datasets, accelerated computational workflows, and enhanced characterization of unculturable microorganisms. However, challenges persist including data complexity management, model interpretability limitations, and the need for standardized datasets and benchmarking protocols. The convergence of machine learning methodologies with metagenomic analysis represents a paradigm shift in microbial community research, offering unprecedented opportunities for data interpretation and biological discovery while presenting unique computational and methodological challenges.