Exposure modeling has become a fundamental component of exposure analysis as it provides an efficient and economical means for assessing exposure of individuals to populations over a variety of spatial and temporal scales for past, current, future, or hypothetical conditions. For airborne particulate matter, traditional modeling approaches typically utilize ambient concentration data to assign exposure levels across an area of interest for a given period of time. Technological advancements have allowed for more sophisticated and innovative modeling approaches that combine exposure measurements and/or models to integrate the strengths of individual methods. The purpose of this article is to provide a general overview of both conventional and novel approaches of modeling exposure to fine particulate matter.