Childhood vaccinations are vital for protecting children from preventable disease and improving overall public health. However, generating reliable estimates of routine immunisation uptake, essential for appropriate policy planning and resource allocation is complicated by various data challenges. A specific challenge in estimating coverage with household surveys such as the National Family Health Survey is that the presence of vaccination is obtained via maternal recall if a health-based record is absent. This study examines the extent to which estimates of childhood immunisation coverage derived using depend on maternal recall: a mother’s ability to correctly identify which vaccines a child has received. In this study, we leverage spatial Bayesian models to estimate routine childhood immunisation rates at sub-national resolutions in 2015 and 2020 using various assumptions about the accuracy of maternal recall. This modelling approach and explicit consideration of maternal recall allows us to identify local regions whose previous estimates of vaccine coverage rates may be overstated due to low rates of the presence of health-based records. We create detailed vaccination coverage maps to analyze the models with and without maternal recall data. By highlighting vaccination “coldspots” and their change over time, this study reveals the potential benefits or limitations of using maternal recall in generating vaccine coverage estimates and provides a basis for more informed decision-making for immunization interventions in India and similar contexts.