Artificial Intelligence for Predictive Microbiome Engineering: Integrating Community Design and Environmental Validation

Microbial ecosystems can now be measured across genomic, transcriptomic, proteomic, metabolomic, and environmental dimensions, yet their emergent functions remain difficult to predict and control. Artificial intelligence (AI) can integrate heterogeneous data, but predictive accuracy alone does not establish causality, transferability, or controllability. This review developed a five-layer framework: observe, infer, predict, design, and learn that connects microbial measurement to prospective intervention and experimental feedback. We critically synthesise evidence from sequence and protein learning, multi-omics integration, metabolic modelling, synthetic microbial communities, environmental biotechnology, and temporal forecasting. Recent primary studies illustrate progress in forecasting community trajectories, predicting microbial competition and designing consortia for pollutant degradation; they also reveal important boundaries between model performance and demonstrated ecosystem control. We examine risks arising from compositional data, information leakage, dependent observations, batch effects, uncertain calibration, sparse perturbation data, ecological instability, and evolutionary change. We propose operational criteria for ecosystem programmability and a staged validation pathway from controlled systems to field-relevant environments. Progress will depend on standardised context-rich data, hybrid AI-mechanistic models, prospective benchmarks, independent replication, and safety-constrained design.