Next-generation cotton improvement through integrated biosynthetic pathways, multi-omics, artificial intelligence and advanced breeding technologies

Cotton ( Gossypium spp.) is a globally important fibre and oilseed crop whose productivity and quality are increasingly constrained by climate change, biotic stresses and the complex genetic architecture of key agronomic traits. Recent advances in genomics, high-throughput phenotyping and systems biology have transformed our understanding of cotton evolution, fibre development, stress adaptation and defence mechanisms. This review synthesizes current progress in cotton improvement through integrative approaches encompassing pan-genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics, with particular emphasis on fibre quality regulation, secondary metabolite-mediated insect resistance and abiotic stress tolerance. We highlight how polyploidy, structural variation, homoeolog expression bias and regulatory network rewiring underpin phenotypic diversity and environmental adaptability in cotton. Furthermore, we discuss the emerging roles of artificial intelligence and machine-learning models in genomic prediction, trait mapping and decision support for precision breeding. The review also examines translational innovations, including CRISPR-based genome editing, nano-enabled delivery systems and nano-formulated inputs for sustainable pest and nutrient management. By integrating multi-omics data with AI-driven analytics, we propose a next-generation framework for accelerating cotton breeding pipelines under changing agro-ecological conditions. Despite rapid progress in Genomics, Bioinformatics, and Plant Breeding, existing reviews lack a systems-level perspective that links data generation, predictive modeling, and targeted intervention. This review addresses this gap by conceptualizing these advances as components of an integrated, data-driven breeding pipeline. This review uniquely integrates multi-omics and AI-driven approaches into a unified framework for cotton improvement, highlighting existing gaps and future opportunities. By integrating genome editing, nano-enabled delivery systems, and predictive analytics, these approaches enhance selection accuracy, reduce generation intervals, and enable high-throughput screening, collectively improving breeding efficiency, reduces breeding cycles and field-level resilience.