Objective This study aims to systematically analyze the construction of intelligent breeding technology systems and their application advances in crop genetic improvement, discuss technical barriers and future development pathways, and provide theoretical support for the intelligent transformation of China’s seed industry.Methods Based on core technological modules such as high-throughput phenotypic and genotypic data acquisition, artificial intelligence (AI) algorithm decision-making, and multi-omics platform integration, this review synthesizes representative case studies and recent advancements from both domestic and international research. It evaluates breakthroughs achieved by intelligent breeding in complex trait design, germplasm resource mining, and genetic gain prediction. Results Intelligent breeding, through the establishment of a multidimensional "genotype–phenotype–environment" data-driven model, has significantly enhanced both the efficiency and precision of breeding processes. For instance, the “Intelligent Breeding Management System” developed by the Chinese Academy of Agricultural Sciences can process up to 100,000 genotypic datasets per day, achieving a genetic analysis speed improvement of over 1,000-fold. The CRISPR-Cas12i gene editing system combined with AlphaFold3-based protein structure prediction has improved target site design efficiency to 98%. Single-cell sequencing and three-dimensional genome analysis have revealed spatial interactions among regulatory elements in maize, providing novel targets for trait improvement. Deep learning models such as RicEns-Net and MMGF, which integrate multimodal remote sensing and meteorological data, have reduced yield prediction errors to below 8.5%.Conclusion The intelligent breeding technology system has demonstrated significant advantages in overcoming traditional breeding bottlenecks. However, challenges remain, including the standardization of multi-source data and limited interpretability of algorithms. Future efforts should focus on strengthening interdisciplinary integration across biology, informatics, and engineering, developing knowledge-embedded algorithms, and establishing global collaborative platforms to accelerate the digital sharing of germplasm resources and the restructuring of commercial breeding systems. It is also recommended to establish industry-university-research collaboration mechanisms through policy guidance to expedite the efficient translation of technological achievements into industrial applications.