With the accelerating commercialization of genetically modified (GM) and gene-edited (GE) crops within global agricultural trade, the accurate identification of potential unintended effects (UEs) in biotechnology-derived breeding products has become a central issue in biosafety assessment. For a long duration, the evaluation system has relied largely on the principle of "substantial equivalence", wherein targeted detection technologies have played a significant role in assessing conventional physicochemical indicators. However, traditional methods struggle to capture complex molecular variations. Moreover, conventional "bulk multi-omics" analysis tends to mask cellular heterogeneity, failing to detect rare variants and their intricate microenvironmental interactions at the microscopic level. This review summarized recent advances in the application of multi-omics technologies for evaluating unintended effects in biotech breeding products. It focused on the potential of emerging technologies such as single-cell omics and spatial omics in deciphering tissue-specific unintended effects. Furthermore, the article provided an in-depth analysis of how non-targeted multi-omics—encompassing genomics, transcriptomics, proteomics, and metabolomics—enable the comprehensive profiling of multi-layered molecular variations through full-spectrum scanning. Finally, the article explored the challenges and prospects of integrated multi-omics data analysis and artificial intelligence (AI) in future biosafety regulatory frameworks. Therefore, a systems biology strategy integrating multi-dimensional omics data, combined with machine learning and artificial intelligence, represents a crucial trend toward achieving comprehensive and precise food safety and unintended effects assessment of biotech breeding products.