Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of an inflammatory response. This response is especially pronounced in the lung, where pulmonary cells sit at the interface between the body’s internal and external environments. Since prolonged exposure to CS has been linked to the development and progression of inflammation-related pulmonary diseases, including COPD, a thorough mechanistic understanding of the initial inflammatory pathways modulated by CS is central to understanding disease pathogenesis. We combined a survey of relevant published literature with the computational analysis of multiple transcriptomic data sets to construct a network model (the Inflammatory Process Network [IPN]) of the main pulmonary inflammatory processes. The IPN model was constructed with a modular architecture that captures different processes occurring in key resident pulmonary cells as well as immune cells recruited from the systemic circulation following exposure to cs. From the resulting 23 cell-type specific subsets of the IPN, selected ones were used to evaluate transcriptomic profiling data from lungs of wild-type cd-1 mice exposed to CS for up to 5 months with regard to the activation of inflammatory signaling mechanisms. This revealed that these submodels showed an increased activation in response to continued CS exposure, and that they added molecular granularity and clarified the role of individual cell types to previously identified inflammatory processes. As with our previously built network models (“cell proliferation” and “cell stress”), the IPN will be freely available and, thus, will provide a novel resource to the scientific community with broad applicability to aid in the investigation of inflammatory signaling mechanisms affected by CS and in the study of pulmonary disease pathogenesis in response to CS exposure.