Background: The role of population-based newborn genetic testing to identify infants at high risk of childhood-onset cancers has not been studied, despite the availability of cancer surveillance guidelines for early detection in high-risk infants and children. Methods: We developed the Precision Medicine Prevention and Treatment (PreEMPT) Model to estimate the value of targeted population-based newborn genomic sequencing (tNBS) for a select panel of genes associated with early onset pediatric malignancy. Cohorts of US newborns were simulated under tNBS screening vs. usual care, from birth to death. Six pediatric cancer predisposition syndromes were included in the model with mutations in RET, RB1, TP53, DICER1, SUFU or SMARCB1 assigned at birth, using mutation prevalence and disease risks drawn from the published literature, as well as SEER, ClinVar and gnoMAD databases. Newborns with mutations underwent cancer surveillance based on established guidelines for each gene-related pediatric malignancy. Survival benefit was modeled as a reduction in proportion of advanced disease, cancer deaths, and treatment-related late mortality risks. Costs were based on published literature and national databases. Results: In a typical US birth cohort of 4 million newborns, we estimated 1280 cancer cases in the malignancies associated with this gene panel would be detected before age 20 under usual care, resulting in 451 cancer deaths and 490 living with radiation exposure risks. tNBS would prevent 8 cancers (in RET mutation carriers), avert 34 deaths through surveillance, result in 3190 life-years (LY) gained and a 13% relative reduction in proportion of adult survivors at risk for radiation-associated late mortality. Given a sequencing cost of $30 (e.g., $5/gene), the incremental cost-effectiveness ratio (ICER) for tNBS was $230,500 per LY saved; if no additional cost was incurred beyond standard newborn screening, the ICER decreased to $101,100/LY. Conclusions: Population-based genetic testing of newborns can reduce mortality associated with pediatric cancers and could potentially be cost-effective as sequencing costs decline. Further work will include modeling a broader panel of predisposition genes.