Microbial communities shape human health through colonization, metabolism, and immune modulation, yet most human-associated microbes remain difficult to culture. This limits our ability to move from sequencing-based association studies to mechanistic “sequence-to-function” understanding and to develop reproducible, strain-defined interventions. Traditional culturomics relies on manual colony picking and ad hoc prioritization, making it labor-intensive, low-throughput, and biased against rare or slow-growing taxa. We present CAMII (Culturomics by Automated Microbiome Imaging and Isolation), an AI-enabled, end-to-end automated platform designed to scale the isolation and characterization of diverse gut microbes. CAMII integrates high-throughput colony imaging, robotic colony picking, and machine learning to guide isolation decisions. These models quantify colony morphology and growth dynamics across plates and prioritize colonies for picking, reducing redundancy and increasing phylogenetic coverage compared to manual workflows. Applied to human fecal samples, CAMII enabled rapid construction of phylogenetically diverse “living libraries” and has now supported generation of a collection of over 30,000 gut microbial isolates, including >1,100 sequenced strains spanning hundreds of species, with previously underrepresented lineages and candidate novel taxa. By coupling robotics with AI-driven prioritization, CAMII provides a scalable roadmap for expanding cultivation of microbiome “dark matter,” enabling genome-resolved isolate resources for functional studies, ecological interaction mapping, and translational applications such as live biotherapeutic development.