Introduction: Ultraprocessed foods are becoming dominant in the global food supply. Prospective cohort studies have consistently found an association between high consumption of ultraprocessed foods and increased risk of several noncommunicable diseases and all-cause mortality. The study aimed to (1) estimate the risk of all-cause mortality for ultraprocessed foods consumption and (2) estimate the attributable epidemiologic burden of ultraprocessed food consumption in 8 select countries. Methods: First, a dose-response meta-analysis of observational cohort studies was performed to assess the association between ultraprocessed food consumption and all-cause mortality and estimated the pooled RR for all-cause mortality per each 10% increment in the percentage ultraprocessed food. Then, the population attributable fractions for premature all-cause mortality attributable to the ultraprocessed foods in consumption were estimated in 8 select countries with relatively low (Colombia and Brazil), intermediate (Chile and Mexico), and high (Australia, Canada, United Kingdom, and the U.S.) ultraprocessed food consumption. Analysis was conducted in November 2023-July 2024. Results: The meta-analysis showed a linear dose-response association between the ultraprocessed food consumption and all-cause mortality (RR for each 10% increase in percentage ultraprocessed food=1.03; 95% CI=1.02, 1.04). Considering the magnitude of the association between ultraprocessed foods intake and all-cause mortality and the ultraprocessed food dietary share number (percentage ultraprocessed food) in each of the 8 selected countries, estimations varied from 4% (Colombia) to 14% (United Kingdom and U.S.) of premature deaths attributable to ultraprocessed food intake. Conclusions: The findings support that ultraprocessed food intake contributes significantly to the overall burden of disease in many countries, and its reduction should be included in national dietary guideline recommendations and addressed in public policies. Am J Prev Med 2025;68(6):1091-1099. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.