The application of machine learning and artificial intelligence in plant metabolic engineering is rapidly advancing, with advanced algorithms being employed to predict gene-phenotype relationships and optimize metabolic designs . These approaches can sift through vast amounts of data to identify patterns and relationships that might not be apparent through traditional analysis methods. Multi-omics data integration, combining transcriptomics, proteomics, metabolomics, and phenomics data, is providing a holistic understanding of metabolic regulation and plant responses to genetic modifications . This integrated approach allows for a more nuanced understanding of the complex interplay between different levels of biological organization. Kinetic modeling of metabolic pathways, developing detailed mathematical models of enzyme kinetics and pathway dynamics, is informing engineering decisions by providing insights into the behavior of metabolic systems under various conditions . Additionally, genome-wide association studies (GWAS) are identifying natural genetic variants associated with metabolic traits, guiding engineering efforts by highlighting key genes and regulatory elements . These computational and systems biology approaches are enabling more rational and efficient metabolic engineering strategies, as demonstrated by recent work in elucidating carbon flux in plant metabolism , and are poised to accelerate the development of improved crops and plant-based production systems.