Investigating fertility trends and identifying their determinants in Iran necessitates access to high-quality, reliable data and the application of advanced analytical methodologies. However, national censuses typically lack comprehensive, direct information on women’s complete birth histories, often reporting only the number of surviving children residing in the household. This limitation poses significant challenges for accurately analyzing temporal fertility dynamics and systematically assessing the effects of socio-economic factors. To overcome these constraints, the present study employs a fertility trend reconstruction method, utilizing a 2% sample of the 2016 census to estimate annual fertility patterns disaggregated by socio-economic characteristics. Following the preparation of data in an event-history format, information is transformed into a person-period structure, whereby each woman contributes a separate record for each year she is at risk of childbearing. This data structure enables the simultaneous modeling and incorporation of time-varying covariates, such as age and calendar year, alongside fixed socio-demographic characteristics. Subsequently, Poisson regression is employed to estimate fertility rates and assess the influence of explanatory variables. This approach enhances the precision of estimates and facilitates the reconstruction of annual fertility trends, yielding time series that can serve as reliable inputs for population projection models, thereby substantially improving the accuracy of forward-looking demographic estimates.