Microbiology and Biotechnology Research: An Overview Vol. 11

Climate-resilient crop improvement is constrained less by the absence of candidate genes than by weak connections among target-environment definition, molecular evidence, predictive modelling and field validation. This critical narrative review examines how artificial intelligence, multi-omics and genome editing can be combined without treating technological convergence as evidence of agronomic effectiveness. Literature published from 1 January 2010 to 4 July 2026 was selected through transparent searches of open scholarly databases and citation networks, with priority given to peer-reviewed primary studies, influential methods papers and high-quality reviews. Artificial intelligence and machine learning improve image-based phenotyping, genotype-by-environment prediction and prioritisation of complex molecular features, but performance is frequently inflated by non-independent validation, population structure, small training sets and environmental domain shift. Multi-omics can expose regulatory modules and stress-responsive pathways, yet integration remains vulnerable to batch effects, temporal mismatch, tissue averaging and correlations that do not establish causality. Clustered regularly interspaced short palindromic repeats and associated genome-editing systems provide efficient routes from candidate targets to altered alleles, including multiplex, base- and prime-editing strategies; nevertheless, transformation dependence, polyploid redundancy, pleiotropy and weak multi-environment testing limit translation. The strongest evidence arises when environmental characterisation, phenomics and genomic prediction are connected to experimentally testable targets and edited lines are evaluated against elite comparators under realistic single and combined stresses. A convergence framework is proposed in which field data recursively update trait definitions and models, while causal perturbation tests the biological interpretations generated from multi-modal data. Near-term progress depends on prospective validation, pangenome-aware models, shared metadata standards, independent field networks and explicit product profiles. Convergence can shorten some stages of crop improvement, but climate resilience remains a systems property that must be demonstrated across environments, management regimes and seasons rather than inferred from molecular or computational performance alone.