Research on the application of multi-omics and artificial intelligence (AI) in sepsis-associated atrial fibrillation (SA-AF) has progressed, yet significant translational bottlenecks remain. Firstly, the depth of multi-omics integration is inadequate. Most analyses are still confined to single-omics approaches, lacking systematic cross-omics integration and dynamic temporal data, which hampers the elucidation of molecular mechanisms and causal relationships. Molecular discrepancies between peripheral blood and atrial tissue further limit the accuracy of biomarkers. Secondly, AI models commonly suffer from weak generalizability, overfitting due to small sample sizes and data bias, and often lack external validation and mechanistic interpretability. Being predominantly static, these models struggle to adapt to the dynamic progression of sepsis. Thirdly, target translation and drug development face challenges, including insufficient target specificity potentially leading to off-target risks, species differences and patient heterogeneity hindering preclinical translation, and unverified safety of precision delivery systems. Future research needs to establish a multi-level, dynamically integrated multi-omics and AI analytical framework, incorporating longitudinal data and causal inference methods (e.g., Mendelian randomization) to validate targets. Furthermore, employing tissue specific inference and single-cell technologies to decipher the immuno-electrophysiological interaction network is crucial to advance the precision prevention and management of SA-AF