Plant microbiome research is a rapidly developing and productive field, with substantial attention directed toward the identification of the major determinants of plant growth, nutrient acquisition, stress tolerance, disease resistance, and associated agricultural productivity. Advances in high-throughput sequencing and multi-omics technologies have generated large and complex datasets describing plant-associated microbial communities. However, the high dimensionality, sparsity, compositionality, and heterogeneity of microbiome datasets create substantial analytical challenges for conventional statistical approaches. Machine learning and artificial intelligence (AI) provide new opportunities to identify microbial biomarkers, predict plant phenotypes, characterize plant–microbe interactions, and design beneficial microbial communities. Applications include plant disease prediction, crop productivity, abiotic-stress tolerance, nutrient cycling, biomarker discovery, microbiome-assisted breeding, and synthetic microbial community (SynCom) design. Recent studies increasingly combine AI with multi-omics, synthetic biology, and predictive modeling, indicating a transition from descriptive microbiome analysis toward microbiome engineering. Nevertheless, model generalizability remains constrained by small sample sizes, compositional data, technical variation, batch effects, environmental heterogeneity, and insufficient external validation. The combination of microbiome science and AI has considerable potential to support sustainable, climate-resilient crop production, but successful translation will require rigorous biological validation and field-scale testing.