Microbiology and Biotechnology Research: An Overview Vol. 10

Climate change is increasing the frequency, intensity and co-occurrence of heat, drought, flooding, salinity and biotic pressures that destabilise crop performance, while conventional breeding cycles remain slow relative to the pace of environmental change. At the same time, crop improvement has entered a data-rich and intervention-rich era in which genomics, pan-genomics, multi-omics, high-throughput phenotyping, genome editing and predictive breeding can be connected by artificial intelligence (AI). This critical narrative review evaluates whether that convergence is producing a coherent route from climate signal to deployable cultivar rather than a collection of parallel technologies. Literature was selected through structured searches of multidisciplinary, agricultural and biomedical scholarly sources, complemented by citation tracing and bibliographic verification. The synthesis indicates that AI contributes most reliably when it addresses a defined breeding decision: extracting field phenotypes, prioritising candidate genes, modelling genotype-by-environment responses, predicting breeding values, or optimising editing designs. Evidence is less convincing for claims that complex deep-learning architectures consistently outperform well-tuned statistical models, or that AI-nominated targets routinely translate into durable, multi-environment field resilience. Genomics and pan-genomics expand the searchable allelic space, phenomics and envirotyping improve measurement of context-dependent traits, genome editing provides a causal perturbation layer, and genomic prediction converts heterogeneous evidence into selection decisions. Their strongest integration therefore resembles a closed learning cycle of observation, inference, perturbation, field validation and model updating. Major constraints include biased training populations, reference-genome and phenotype bias, environmental domain shift, limited causal identification, transformation bottlenecks, pleiotropy, insufficient multi-location validation, weak uncertainty propagation, data interoperability and unequal access to digital and molecular infrastructure. Future progress should prioritise prospective breeding-scale evaluations in which integrated AI-biotechnology pipelines are judged by genetic gain, stability across target populations of environments, cycle time, cost and deployability. The convergence is scientifically credible, but its transformative value will depend less on algorithmic novelty than on rigorous biological validation and decision-centred integration across the breeding pipeline.