Delicatessen meats are reported to be the leading vehicle of foodborne listeriosis outbreaks with a high fatality. Predictive models are valuable tools to assess and manage public health risks. The objective of this study was to develop and validate a global model based on food matrices' literature data for predicting the growth of Listeria monocytogenes in processed meat products. The model considers the effect of seven environmental parameters including temperature, pH, water activity, nitrite, acetate, lactate, and propionate with and without interaction effects. Experimental growth curves of Listeria in food matrices (beef, pork, and poultry) were collected from ComBase, or modeled from research publications, and analyzed, after which a total of 414 growth curves were retained. The four-parameter logistic model with delay was used as the primary model to estimate the growth parameters (No, Nmax, tlag, µmax) for each curve. The growth rate data were divided into two sets, about 355 data were used to build the model, and 135 food matrix datasets for validation. A gamma concept-based bespoke model was developed and performance was evaluated using bias factor (Bf), accuracy factor (Af), and acceptable simulation zone (ASZ) of ± 0.5 log cfu/unit. The developed global model had an acceptable Bf of 1.02, Af of 1.17, and ASZ score was more than 72%. Therefore, the gamma secondary model developed using food matrix data provided a valid prediction for the growth of Listeria in processed meat products. This study is the first to develop a model that considers nitrite, propionate, acetate, and lactate as combined inhibitors of Listeria, along with temperature, pH, and water activity. The results of our study provide significant insights into the need for the development and validation of predictive models in real food matrices rather than laboratory media alone. The model may be useful in timely decision-making and quantitative risk assessment in RTE-cooked meat products.