Context: Ground-nesting bees are essential pollinators in natural and urban ecosystems, yet their nesting habitats are increasingly threatened by anthropogenic activities such as urbanization and agricultural practices. Although local ecological drivers of nesting are well understood, applying this knowledge at the landscape scale remains challenging due to limited field data, the spatial scale at which nesting aggregations occur and spatial heterogeneity in urban environments. Objectives: This study uses a Bayesian framework to combine landscape, soil, vegetation, and disturbance related predictors with prior ecological knowledge to model habitat suitability for ground-nesting bees across an urban landscape. We illustrate this approach for the grey-backed mining bee (Andrena vaga) in Braunschweig, Germany. Methods: We derived spatial indicators representing known nesting habitat factors (floral resource proximity, sun exposure, soil texture, vegetation density, probability of tillage, and flood risk) from remote sensing and GIS datasets. A Bayesian logistic regression model integrated these indicators with 55 observed nest locations and literature-based priors to generate habitat suitability and uncertainty maps. Results: The resulting model achieved an average fivefold spatial cross-validation ROC-AUC score of 0.76 (95% posterior credible interval: 0.70–0.81) and provided spatially explicit estimates of prediction uncertainty. Sparsely vegetated areas with high insolation proved most suitable, yet all environmental variables were important. The model outputs form a decision-support layer for identifying and prioritizing suitable nesting areas in urban planning contexts, incorporating both predicted nesting probability and associated uncertainty. Conclusions: By integrating ecological knowledge through informative priors, the Bayesian framework enables robust habitat suitability assessment even with limited field observations, reducing overfitting risks common in data-sparse urban ecological studies. Importantly, our approach induces spatially explicit uncertainty estimates that highlight areas where predictions are less supported by observed data. This offers critical guidance for targeted conservation planning in areas with the highest potential. The framework’s dependence on widely available remote sensing and GIS data suggests potential transferability to other ground-nesting bee species and cities, though validation across contrasting urban landscapes will be needed to confirm this.