Pediatric adherence is strongly shaped by whether a child can tolerate the taste, texture, and aftertaste of an oral medicine. Liquid formulations are especially complex because bitterness, sweetness, viscosity, and aroma are experienced together rather than as isolated attributes. Current palatability development often depends on expert judgment, small sensory panels, and iterative reformulation. These approaches can identify unacceptable products, but they do not reliably predict how a planned change in sweetener, bitterness masking, or viscosity would affect future child acceptability. This predictive modeling article proposes a machine learning framework for estimating pediatric liquid palatability from sweetener characteristics, API bitterness, viscosity, and prior acceptability data. The intended outputs are a predicted acceptability score, a rejection-risk indication, and interpretable formulation guidance. A gradient-boosted regression and classification framework is proposed for curated formulation records containing sweetener identity and concentration, electronic-tongue or sensory bitterness measurements, rheological descriptors, and historical pediatric or caregiver acceptability observations. The model is conceptual and is intended to guide study design rather than report experimental findings. Conceptually, the model could identify whether bitterness intensity, sweetener potency, sweetener concentration, viscosity, or their interactions are most responsible for a predicted palatability limitation. It would be expected to support virtual screening of formulation variants before pediatric panel testing. Predictive palatability modeling could reduce reliance on late-stage trial-and-error in pediatric liquid formulation development. A standardized dataset linking objective measurements with human acceptability outcomes would be essential for reliable implementation.