<ns3:p>Linking high-dimensional omics data to measurable traits remains a major challenge in livestock biology. We introduce a network-based multi-omics integration workflow that connects muscle transcriptome profiles with carcass and meat-quality traits in a modular and reproducible manner. The workflow proceeds through four stages: (i) RNA-seq preprocessing and normalization; (ii) differential expression and clustering; (iii) pathway-level functional enrichment combined with network topology analysis; and (iv) a trait-guided layer that integrates machine-learning–based feature prioritization with rank-based correlation analysis. Instead of focusing on single-gene effects, the framework emphasizes network-level interpretation and trait context, improving biological interpretability and reducing noise from marginal statistics.</ns3:p> <ns3:p>When applied to a dataset generated under sustainable feeding strategies, transcriptomic variation alone showed limited separation, yet network-level modeling revealed coherent gene communities associated with metabolic and regulatory adaptation. Incorporating trait information enabled prioritization of modules linked to measurable phenotypes, providing insight into how nutritional intervention reshapes regulatory architecture.</ns3:p> <ns3:p>Rather than reporting a list of differentially expressed genes, this framework offers a practical route to convert omics observations into biological hypotheses. By revealing regulatory modules associated with phenotypic variation, it supports downstream functional validation and decision-making. The conceptual design can be adapted to other omics contexts where molecular data need to be anchored to traits, making the approach applicable across agricultural, biomedical, and environmental research domains.</ns3:p>