Greenhouse gas emissions (GHGe) data are predominantly available for agricultural commodities and broad dietary patterns. However, data on commonly consumed, multi-ingredient food products as sold in supermarkets and retail outlets are mostly absent. We developed a novel approach to estimate product-specific GHGe for individual packaged foods and beverages marketed in Australia in 2019. Ingredient lists for 23,550 packaged products were systematically disaggregated and a linear program was used to estimate ingredient proportions in each product. Ingredient-specific, cradle-to-farm gate GHGe values were identified from five life cycle assessment data sources. Weighted GHGe values for each ingredient were summed up to a product-specific GHGe estimate that was then additionally adjusted for processing and transport-related emissions to account for most attributable emissions from cradle-to-retail. To assess validity, GHGe for food categories, estimated as the median of product-specific GHGe estimates, were compared against existing GHGe values for analogous food groups. The included products contained 897 different ingredients for which GHGe values were determined using data from 433 life cycle assessments. The estimated median for cradle-to-retail GHGe for all products was 2.35 kg CO2eq/kg product (interquartile range, IQR, [1.24, 4.53]). Product-specific GHGe estimates varied substantially within food categories; the 25th and 75th percentile of GHGe values differed by 2-fold or more for most. ‘Meat and meat products’ had the highest median GHGe value and broadest range of estimates (6.81 kg CO2eq/kg product, IQR [5.84, 29.2]). ‘Fruit, vegetables, nuts and legumes’ had the lowest median GHGe (1.20 kg CO2eq/kg product). Median category values for product-specific GHGe estimates aligned well with values for 34 food groups previously reported (R2 = 98.6%). Our approach provides a novel method for estimating product-specific GHGe for Australian packaged foods and beverages. The study enabled clear differentiation between products within a given category due to the unique ingredient proportion estimation method, while retaining good comparability against values derived using existing methods. These data have significant potential for driving industry actions and informing government policies.