Abstract <p>Crop breeding has entered a new era with the development of high-throughput technologies, such as genomics, transcriptomics, and single cell omics. However, effective utilization of these technologies depends critically on the integration of heterogeneous data from diverse repositories, which is essential for comprehensively understanding the complex biological processes underlying plant traits and their interactions. This review emphasizes the pivotal role of multi-omics data integration in modern crop improvement strategies. We delve into the available omics data and resources, outline the challenges in integrative bioinformatics, and present approaches to model the complex network of coding and non-coding RNAs within biological systems across spatial and temporal scales. Additionally, we leverage artificial intelligence including machine learning and deep learning technologies to unravel the intricacies and molecular mechanisms of whole plant cells and their interactions. In summary, this review seeks to provide valuable insights into the integration of multi-omics data in crop breeding and to illustrate the power of integrative bioinformatics in fostering a comprehensive understanding of biological processes and their interactions at various scales.</p>