Prenatal inorganic arsenic (iAs) exposure influences the expression of critical genes and proteins associated with adverse outcomes in newborns, in part through epigenetic mediators. The doses at which these genomic and epigenomic changes occur have yet to be evaluated in the context of dose–response modeling. The goal of the present study was to estimate iAs doses that correspond to changes in transcriptomic, proteomic, epigenomic, and integrated multi-omic signatures in human cord blood through benchmark dose (BMD) modeling. Genome-wide DNA methylation, microRNA expression, mRNA expression, and protein expression levels in cord blood were modeled against total urinary arsenic (U-tAs) levels from pregnant women exposed to varying levels of iAs. Dose–response relationships were modeled in BMDExpress, and BMDs representing 10% response levels were estimated. Overall, DNA methylation changes were estimated to occur at lower exposure concentrations in comparison to other molecular endpoints. Multi-omic module eigengenes were derived through weighted gene co-expression network analysis, representing co-modulated signatures across transcriptomic, proteomic, and epigenomic profiles. One module eigengene was associated with decreased gestational age occurring alongside increased iAs exposure. Genes/proteins within this module eigengene showed enrichment for organismal development, including potassium voltage-gated channel subfamily Q member 1 (KCNQ1), an imprinted gene showing differential methylation and expression in response to iAs. Modeling of this prioritized multi-omic module eigengene resulted in a BMD(BMDL) of 58(45) μg/L U-tAs, which was estimated to correspond to drinking water arsenic concentrations of 51(40) μg/L. Results are in line with epidemiological evidence supporting effects of prenatal iAs occurring at levels <100 μg As/L urine. Together, findings present a variety of BMD measures to estimate doses at which prenatal iAs exposure influences neonatal outcome-relevant transcriptomic, proteomic, and epigenomic profiles.