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The rapid advancement of multi\u2010omics technologies has broadened biomarker discovery from single genomics to multidimensional integrative analyses encompassing transcriptomics, proteomics, and metabolomics. This progression offers opportunities to improve disease diagnosis, subtyping, prognosis assessment, and treatment monitoring. However, translational gaps persist between multi\u2010omics discoveries and clinically applicable biomarkers. This review systematically examines the current application of multi\u2010omics biomarkers in genetic neuromuscular diseases. It provides an in\u2010depth analysis of the multifaceted barriers encountered during the translation process, including technical hurdles, clinical validation complexities, data interpretation challenges, and health system\u2010level obstacles. Furthermore, the review explores emerging solutions including artificial intelligence\u2010assisted decision\u2010making, ethical governance, and policy preparedness. 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