The Plant Microbiome in a Changing World: Ecology, Evolution, Stress Resilience and Sustainable Applications

Plant-associated microbiomes influence nutrient acquisition, development, disease resistance and stress responses, yet their context dependence has made reliable prediction and engineering difficult. This critical narrative review evaluates whether the convergence of multi-omics, machine learning and synthetic-community experimentation is moving plant microbiome research from descriptive association towards predictive design. Literature published principally from 2015 to 15 July 2026 was examined, with earlier foundational studies retained where necessary. The synthesis focuses on paired host–microbiome measurements, integrative statistical and artificial-intelligence methods, experimentally tractable synthetic communities, ecological and metabolic modelling, and validation across plant genotypes and environments. Multi-omics has substantially improved resolution by connecting community composition with gene activity, metabolites, host molecular states and spatial context, but integration remains vulnerable to compositionality, batch effects, sparse sampling and mismatched temporal scales. Machine-learning studies now show that microbiome and multi-omic features can predict crop productivity, disease status and stress-associated phenotypes, while interpretable models can identify condition-dependent cross-omic features. Nevertheless, prediction is often easier than transportability: many models are evaluated within a single experiment, site or host background, and feature importance does not establish causal interaction. Synthetic communities provide the strongest bridge from association to mechanism because candidate taxa and functions can be perturbed directly, but simplified consortia may lose emergent properties or fail to establish in resident field communities. The evidence therefore supports a design–build–test–learn framework in which artificial intelligence prioritises hypotheses, mechanistic models constrain them, and multi-environment perturbation tests determine whether predicted functions are stable and causal. Predictive microbiome design is technically plausible and increasingly evidence-based, but its agricultural value will depend on benchmarked datasets, explicit uncertainty, ecological validation and reproducible standards rather than model complexity alone.