Background: Pregnancy identification and follow-up surveillance can enhance the reporting of pregnancy outcomes, including stillbirths and perinatal and early postnatal mortality. This paper reviews pregnancy surveillance methods used in Health and Demographic Surveillance Systems (HDSSs) in low- and middle-income countries. Methods: We searched articles containing information about pregnancy identification methods used in HDSSs published between January 2002 and October 2019 using PubMed and Google Scholar. A total of 37 articles were included through literature review and 22 additional articles were identified via manual search of references. We reviewed the gray literature, including websites, online reports, data collection instruments, and HDSS protocols from the Child Health and Mortality Prevention Study (CHAMPS) Network and the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). In total, we reviewed information from 52 HDSSs described in 67 sources. Results: Substantial variability exists in pregnancy surveillance approaches across the 52 HDSSs, and surveillance methods are not always clearly documented. 42% of HDSSs applied restrictions based on residency duration to identify who should be included in surveillance. Most commonly, eligible individuals resided in the demographic surveillance area (DSA) for at least three months. 44% of the HDSSs restricted eligibility for pregnancy surveillance based on a woman's age, with most only monitoring women 15-49 years. 10% had eligibility criteria based on marital status, while 11% explicitly included unmarried women in pregnancy surveillance. 38% allowed proxy respondents to answer questions about a woman's pregnancy status in her absence. 20% of HDSSs supplemented pregnancy surveillance with investigations by community health workers or key informants and by linking HDSS data with data from antenatal clinics. Conclusions: Methodological guidelines for conducting pregnancy surveillance should be clearly documented and meticulously implemented, as they can have implications for data quality and accurately informing maternal and child health programs.