Vultures are a specialized species group, utilizing wide habitat and forage niches and their long-term survival depends on the protection of their critical habitats. Taking a landscape approach, we modelled the distribution of nest sites (n = 30) and roost sites (n = 31) of cliff-nesting vultures (four species) in the Greater Panna Landscape (GPL), central India. We performed Random Forest (RF), Generalized Linear Model (GLM) and Boosted Regression Tree (BRT) algorithms. The AUC values for the predictive distribution of nests were 0.97, 0.90, 0.97 for RF, GLM and BRT, respectively, while for roost distribution it was found to be 0.76, 0.63, 0.74 for RF, GLM and BRT, respectively. We ensembled the predictions of all three methods for better accuracy and combined the model outputs. We then performed zonation analysis on the final map and used Human footprint as a proxy for conservation cost to define spatial prioritization for conservation inputs. The results reveal that the GPL has a total of 9,402 sq. km. area within the top 20 ranks in terms of conservation prioritization for nesting and roosting. Given the cost value, the top 20 ranked units will account for approximately 60% of the critical habitats and these may be the focus of long-term conservation inputs to sustain the vulture populations in the landscape. The spatially explicit outputs based on the robust methodology involving intensive fieldwork and ensembled modelling offer a basis for local scale and landscape scale actions, which can be replicated in other parts of the vulture distribution ranges.