In a world where the global population continues to grow and natural resources are increasingly limited, ensuring food security has become one of the greatest challenges of our time. Climate change, soil degradation, and agro-biodiversity loss have intensified threats to agricultural systems, which are already under significant pressure to produce more food in less space and with a reduced environmental impact. Considering these difficulties, achieving the Sustainable Development Goal of Zero Hunger is an urgent necessity to secure the health and stability of future generations. In this context, crop genetic breeding plays a fundamental role, not only by increasing yields but also by developing varieties resilient to changing conditions. Modern biotechnology tools, such as genomics, and more recently, machine learning (ML), a branch of artificial intelligence (AI), have paved an innovative path in this endeavor. ML enables systems to learn and improve automatically from data, without explicit programming, which has revolutionized multiple fields, including agriculture, by enabling the analysis of large volumes of genomic data to identify complex patterns that predict phenotypic traits, such as disease resistance or productivity under stress, or optimize demographic clustering. Specifically, supervised ML algorithms may enhance genomic prediction accuracy while unsupervised methods reveal deeper population structure and genetic diversity for pre-breeding. Meanwhile, pangenomics is addressing single-reference genome biases and missing heritability by incorporating structural variants (SV), copy number variation (CNV), and transposable element (TE) diversity into genomic analyses. Genome graph models, boosted by long-read sequencing, improve pangenomic variant detection, mapping accuracy, and heritability estimates for complex traits. Nowadays, the development of “super-pangenomes”, which merge cultivated and wild relatives, is further expanding the reservoir of adaptive and consumer-preferred traits. Together, ML and pangenomics offer a powerful framework to speed up crop improvement, prioritize germplasm conservation, and harness genomic diversity for climate-resilient and sustainable agriculture. The integration of comparative genomics through pangenomics and ML will ultimately accelerate the identification of improved varieties and optimize decision-making in agriculture, driving a crucial shift toward more precise and resilient agricultural practices.