Listeria monocytogenes poses health risks in food preparation and storage, as it leads to Listeriosis, primarily affecting newborns and pregnant, elderly, or immunocompromised persons. To mitigate this threat, the United States Food and Drug Administration has established a zero tolerance policy for Listeria monocytogenes in foods. Hazard Analysis Critical Control Point (HACCP), current Good Manufacturing Practices (cGMP), and FDA Guidance Documents define a code of conduct for all food manufacturers. In addition to these strict protocols, predictive microbiological models provide an extra measure of safety, helping identify storage conditions which could allow for the growth of pathogens such as Listeria monocytogenes. Listeria monocytogenes is able to grow or survive in a wide variety of temperature and pH conditions. While the individual effects of either temperature or pH alone may be sufficient to prevent growth in some cases, a combination of these growth inhibiting factors is able to provide greater reduction. Not all models are able to capture the combined effects of inhibiting factors. Existing models either cover the combination of sub-optimal temperature and pH, or the effect of temperature alone over super- and sub-optimal ranges, without pH. One effort of this study examines previous models for the independent effects of temperature and pH, and proposes new models for these independent effects. Building from this, the second effort of this study describes a growth rate surface for the combined effect of both temperature (super- and sub-optimal), as well as pH. This is our novel contribution to predictive microbiological modeling. We compare the novel growth rate surface against the linear-Arrhenius model of Davey for sub-optimal temperature and pH, rooted in the principle of activation energy from reaction kinetics. Our model provides more accurate predictions to be made over a wider range of temperature and pH values.