Obtaining accurate estimates of occupational exposures is a critical objective in epidemiological studies involving working populations. Job-Exposure Matrices (JEMs) have served this purpose for decades, particularly when individual-level exposure data are unavailable, for chemical then other exposure. A JEM is mostly a contingency table that assigns exposures to a large cohort of workers based on their job categories. This approach helps mitigate biases related to symptoms or patient status. Additionally, JEMs are helpful for assessing past exposures, where direct measurement is impossible, and relying on memory may introduce classification errors. Developing a JEM involves several steps. Firstly, the design must be defined: is it based on existing data or expert opinion? What job classification system will be utilized? What components of exposure will be considered? How will other factors be incorporated, such as time, place, gender, and prevention …? Once the framework is established, the matrix is constructed accordingly. Subsequently, its reliability needed to be test through validation studies, and process usually take years. However, users should be mindful of the limitations associated with JEMs. These matrices represent an “average” level of exposure and may not capture the individual variability within the same job category. Moreover, caution must be exercised due to the inherent limitations in their construction and job coding before using such tools. Nevertheless, JEMs offer numerous advantages for researchers, as well as public health practitioners, and occupational health professionals. They provide a systematic approach to estimating exposures and are readily available for certain occupations at collective level.