Pregnant and lactating individuals are both medically complex and critically understudied populations. Despite high rates of both sweetener and medication use, these populations are understudied and frequently excluded from trials, resulting in widespread knowledge gaps and a lack of evidence driven recommendations for use. This dissertation utilizes pharmacometric approaches to help inform recommendations for use of low calorie sweeteners (LCS) and prescription medications. Specifically by (1) quantifying LCS transfer into breast milk and resultant infant exposure, and (2) establishing a physiologically based pharmacokinetic (PBPK) framework to prioritize medications for pharmacokinetic study during pregnancy. In the first project, a prospective population pharmacokinetic study of 40 breastfeeding mother-infant dyads was designed to evaluate infant exposure to two widely consumed LCSs: acesulfame potassium (Ace-K) and sucralose. Serial plasma and milk sampling allowed for the identification of two distinct pharmacokinetic profiles: Ace-K exhibited rapid milk transfer (Cₘₐₓ: 373 ng/mL, milk-to-plasma ratio 1.75), while sucralose showed delayed, more limited transfer (Cₘₐₓ: 7.2 ng/mL, milk-to-plasma ratio 0.15). Infant relative dose estimates were 1.59% for Ace-K and 0.04% for sucralose. To match the concentrations of reported health concerns mothers would have to consume at least the equivalent sucralose of 4x or the equivalent Ace-K of 17x the amount present in the study beverage daily. The second project applied pregnancy-specific PBPK modeling to support systematic prioritization of PK studies in pregnant individuals. A retrospective analysis of medication orders from electronic health records identified 327 unique medications used across trimesters, with 44% exhibiting trimester-specific dose variation. Five model drugs—cefazolin, metformin, glyburide, buprenorphine, and rifampin—were used to construct and validate mechanistic non-pregnant and pregnant PBPK models. Simulations identified drug properties most likely to be predictive of clinically significant exposure shifts, including transporter-dependent clearance, and enzyme-specific metabolism. These insights were operationalized into renal and hepatic decision trees to flag drugs most likely to require dose adjustment during pregnancy. This framework supports the start of a reproducible, mechanism-informed triage of medications for further study and a scalable strategy for improving dosing guidelines in pregnant populations. Together, these projects demonstrate the power of pharmacometric modeling to address unmet clinical needs in maternal-infant health.