Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.

Environmental challenges such as drought, salinity, heavy metal contamination, and nutrient deficiencies threaten global agricultural productivity and food security. These stressors drastically reduce crop yields, necessitating innovative solutions. Recent advancements in omics-based research-spanning genomics, metabolomics, proteomics, transcriptomics, epigenomics, and phenomics-have transformed our understanding of plant stress responses at the molecular level. High-throughput sequencing, mass spectrometry, and computational biology have facilitated the identification of stress-responsive genes, proteins, and metabolites critical for enhancing plant resilience. This review evaluates omics-driven strategies for improving crop performance under environmental stress. It emphasizes multi-omics data integration, precision breeding, artificial intelligence (AI) in crop modeling, and genome-editing technologies. Notably, breakthroughs in machine learning and AI have refined predictive modeling, enabling precise selection of stress-tolerant traits and optimizing breeding strategies. Despite these advancements, challenges remain, including the complexity of multi-omics data analysis, high technology costs, and regulatory barriers. Bridging the gap between research and practical applications requires developing cost-effective platforms, enhancing AI-driven models, and conducting large-scale field validations. This review highlights the transformative potential of omics technologies to develop climate-resilient crops. By integrating these advanced methodologies, agriculture can achieve sustainable food production and bolster global food security in the face of climate change and environmental stressors.