<p>African White-backed Vultures (AWbV) play a vital ecological role as nature’s scavengers, yet their population is declining due to poisoning, habitat loss, human disturbances, and climate variability. This study investigates the spatial and temporal distribution of AWbV in Hwange National Park, Zimbabwe, using a suite of species distribution modelling algorithms. Four modeling techniques, Random Forest, XGBoost, Generalized Linear Models, and an Ensemble approach were applied to assess how the spatial distribution of the AWbV vary across seasons and compare modelling results from the four algorithms The study also tested the predictive performance of each of the algorithms. The study revealed that AWbV in Hwange National Park consistently preferred northeastern regions year-round, with seasonal use of northwestern areas in the dry season likely due to shifting carrion and water resources. Random Forest outperformed other models with near-perfect accuracy, identifying Mean temperature of driest quarter (Bio9) and distance to park boundary (DTPB) as key dry season drivers, while the Maximum temperature of warmest month (Bio5) and Precipitation of wettest month (Bio13) dominated wet season predictions. Based on these results, conservation managers may need to prioritize the deployment of more conservation resources to protect the northeastern hotspot, while also monitoring climate thresholds to anticipate habitat disturbances. The findings highlight the effectiveness of integrating several algorithms in ecological modeling to inform actionable strategies for vulture conservation amidst environmental and anthropogenic pressures.</p>