This study presents an optimized protocol for acquiring and functionally annotating nuclear magnetic resonance (NMR) metabolic fingerprints of Mizuhopecten yessoensis. To overcome the challenge of low metabolite annotation rates in NMR-based metabolomics, we developed an integrated annotation approach combining NMR and transcriptomic data using Weighted gene co-expression network analysis (WGCNA). Through an orthogonal experimental design, we determined the optimal metabolite extraction parameters to be 1∶1 methanol/water, 90 s homogenization, D₂O at pH 7.4, yielding the highest number of detectable NMR signal peaks with over 90% reproducibility in both qualitative and quantitative analyses. Application of this protocol to various tissues of M. yessoensis revealed distinct metabolic profiles for each tissue. Notably, the hepatopancreas and striated muscle showed particularly higher levels of metabolism, such as fructose, fucose, and glucose, as well as energy metabolism-related substances like glycogen, ATP, acetoacetate, and carnitine. By constructing gene-metabolite co-occurrence network using transcriptomic and NMR fingerprint data, we identified metabolite-gene modules that were closely associated with specific tissues. The gene ontology (GO) annotations of genes within these modules were highly consistent with the physiological functions of corresponding tissues. This study provided a robust analytical framework for the functional annotation of NMR spectral and laid a foundation for applying NMR metabolomics in molluscan breeding programs.