Microbiolomics—the comprehensive integration of all omics disciplines associated with microbial systems—encompasses metagenomics, metatranscriptomics, metaproteomics, metareplicomics, metaresistomics, and other phenomic layers that together illuminate microbial community dynamics. However, traditional computational methods face significant limitations in scale, speed, and sophistication when analyzing such high-dimensional data. We present a framework that combines artificial intelligence with microbial phenomics to deliver real-time clinical decision support. Using data from 82 preterm infants (400 gut microbiome samples), AI-driven analysis of bacterial replication rates (Peak-to-Trough Ratios) and resistome profiles predicted late-onset sepsis 3–7 days before clinical symptoms. Infants showing high Klebsiella pneumoniae PTR (>2.0) experienced 67% infection rates, while those with low PTR (<1.5) remained 89% infection-free, enabling pre-emptive targeted interventions that reduced hospital stays. Bayesian network modeling further uncovered causal links between antibiotic exposure, maternal factors, and microbiome shifts, informing antibiotic stewardship modifications. This approach reduced analysis time from months to hours while maintaining analytical depth. The presentation explores the evolving role of large language models in microbiology, providing practical strategies for responsible AI adoption including free tools, validation frameworks, and prompt engineering techniques. We emphasize the Golden Rule: all AI outputs require human validation and intellectual ownership. Ultimately, the future of microbiology lies in synergistic human–AI collaboration, uniting biological insight with computational power to achieve discoveries once beyond reach.