Background and Objective: Real-world evidence (RWE) is essential for estimating influenza vaccine effectiveness (IVE) in wide-ranging high-risk groups by time, place and epidemic strain. Among high-risk groups, pregnant women are at increased risk for influenza-associated morbidity, hospitalization and mortality, and their vaccination is a highest public health priority. However, monitoring IVE among pregnant women remains challenging in nonrandomized studies. Since data from test-negative case-control designs (TND) are widely used for assessing IVE, this literature synthesis aims at synthetizing the existing evidence about their use in pregnant populations. Methods: A structured literature search was conducted on PubMed/Medline, Scopus and grey literature through November 2022, following PRISMA guidelines. IVE — calculated as 1 - odds ratio × 100% — was pooled through meta-analysis. Risk of bias was assessed using the ROBINS-I tool. Results: Nine studies conducted during the 2019/10–2017/18 influenza seasons were included, of which three were specifically designed to investigate IVE during pregnancy and were pooled in the meta-analysis. The pooled estimate of IVE from TND studies was 60% (95% confidence interval 45–75; I2 = 64%) against medically-attended laboratory-confirmed influenza illness. The remaining six reports included ‘pregnancy’ in surveillance-based studies, and found that it was negatively associated with vaccination uptake and positively with hospitalization with confirmed influenza. Overall, studies were considered at low/moderate risk of bias using the ROBINS-I tool. Conclusions: Pooling data from RWE to estimate IVE against severe outcomes in pregnant women is crucial to inform vaccination policy. While TND offers notable advantages in estimating IVE – such as minimizing outcome misclassification of the disease –, novel well-conducted TND are need to provide reliable estimates of IVE according to influenza subtype and vaccine type. Finally, surveillance systems that incorporated TND should expand data collection to better account for pregnancy.