Microbial communities, encompassing a vast taxonomic diversity, are fundamental to ecosystem integrity, biogeochemical cycles, and the health of humans, animals, and plants, along the One Health concept. A major scientific goal is to understand how these complex consortia function, interact, and adapt to environmental changes. Microbial meta-metabolomics has emerged as a powerful approach to tackle this by characterizing the collective metabolome of an entire community, linking it to environmental conditions and biogeochemical processes. It captures the functional output of both cultivable and uncultivable organisms, tracing chemical interactions and the impact of environmental perturbations. However, while meta-metabolomics provides a comprehensive snapshot of community chemistry, it alone cannot decipher the precise dynamics of which microorganisms are producing metabolites, when, where, and why. To address this, we propose the Microbial Metabolomics Framework (MiMetWork). This novel framework expands beyond descriptive meta-metabolomics to integrate spatial and temporal metabolomic characterizations with other omics data and phenotyping techniques. MiMetWork employs high-throughput screening of various microbiome components—from single cells to complex communities—under controlled conditions to elucidate ecophysiological functions and interaction mechanisms. By combining untargeted and targeted metabolomic datasets with microbial composition and pathway information, MiMetWork aims to build causal models of microbiome function and adaptation. This review outlines how this integrative framework leverages technological advances to elucidate microbiome interactions and functional responses across human, animal, and environmental niches, thereby addressing critical research gaps and enhancing our predictive understanding of microbiomes within the One Health paradigm.< Réduire