Striped bass (SB, Morone saxatilis) are an important aquaculture fish as a parental species of hybrid striped bass, the fourth largest US finfish aquaculture industry, and an emerging standalone industry. SB have been bred in captivity for superior production traits (i.e., domesticated) for decades in the National Program for Genetic Improvement and Selective Breeding for the Hybrid Striped Bass Industry with noteworthy success in areas such as spawning without exogenous hormone compounds and reducing time to market size. Despite gains in SB production traits made through breeding, subgroups of SB exhibiting inferior growth and overall failure to reach the desired market size are consistently present in each cohort. An integrated machine learning analysis of fifth generation domestic SB (N=72 fish) transcriptomes and metabolomes was conducted in order to better understand this discrepancy in growth at the cellular level and to identify targets for future breeding efforts and/or biotechnological interventions. The SB sampled were from half-sibling families produced by crossing two female SB (‘dam’) with six male SB (‘sire’) each. Dams were crossed with three sires categorized as “Large” (by weight and length) and three as “Small”, to enable a comparison of omics profiles of SB produced from sires of distinct growth phenotypes. Transcriptomes were generated from fast-twitch, white muscle tissue of eighteen month old SB reared in a recirculating aquaculture system (RAS) and metabolomes were generated from the liver of the same SB. The machine learning analysis identified between 35–300 muscle gene transcripts as determinant of growth performance, dam, sire, or sire size and a pathway analysis of these gene expression patterns revealed distinct differences in critical metabolic pathways. Specifically, genes up-regulated in SB exhibiting inferior growth suggest that protein ubiquitination and skeletal muscle degeneration is active in these fish, rather than critical signaling pathways that regulate genes involved in growth (e.g., HIF-1α Signaling, JAK/STAT) and muscle homeostasis, which were up-regulated in fish of superior growth. The machine learning analysis identified between 25–122 liver metabolites as determinant of SB growth performance. The determinant metabolites identified were predominantly sphingolipids, which are involved in multiple functions such as tissue development, cellular proliferation and differentiation. These metabolites were generally present in higher concentration in the fish of inferior growth, suggestive of differences in synthesis and degradation processes related to liver dysfunction between SB growth performance groups. This metabolite analysis when integrated with the transcriptomic analysis is consistent with observed gene expression differences in the white muscle, suggesting muscle wasting in inferior growth fish. The causes of this dysfunction, whether genetic, dietary, or husbandry factors, remain unclear and therefore are a recommended topic of future research and breeding efforts.