<p>Plant tissue culture underpins plant biotechnology by enabling clonal propagation, somatic embryogenesis, genetic transformation, germplasm conservation, and in vitro production of valuable metabolites. Its broader application, however, remains constrained by genotype-dependent recalcitrance, limited reproducibility, and largely empirical protocol optimization. Recent advances in omics technologies including genomics, transcriptomics, proteomics, metabolomics, and emerging single-cell and spatial omics have greatly improved understanding of the molecular regulation of in vitro responses. In parallel, artificial intelligence (AI) and machine learning (ML) approaches have enabled efficient analysis of high-dimensional omics data and their translation into predictive outcomes. This review synthesizes recent progress at the interface of omics and AI in plant tissue culture, highlighting tangible achievements such as ML-based prediction of regeneration and embryogenic competence with reported accuracies often exceeding 90%, AI-assisted optimization of culture media and hormonal regimes that improve regeneration efficiency and reduce experimental iterations, and AI-guided bioprocess strategies that enhance secondary metabolite yields in plant cell cultures. We emphasize how integrating multi-omics data with AI models supports biomarker discovery, reduces genotype dependency, and improves reproducibility. Finally, we outline future pathways involving integrated omics AI pipelines, digital phenotyping, digital twins of tissue culture systems, and standardized data-sharing practices, positioning omics-AI integration as a transformative framework for next-generation crop improvement and sustainable plant biotechnology.</p>