Taxus baccata L. (European yew) is a relict conifer with a fragmented distribution in North Africa, where its Algerian populations are restricted to humid and sub-humid mountain ranges. Increasing anthropogenic pressure and climate change threaten its persistence, making predictive modeling essential for conservation planning. We applied species distribution modeling (SDM) using the sdm package in R to estimate the current and future distribution of T. baccata in Algeria using an ensemble approach that combined Random Forest (RF), Support Vector Machines (SVM), and Boosted Regression Trees (BRTs). Occurrence data were compiled from GBIF and the literature, while current and future bioclimatic predictors were obtained from WorldClim. Future projections were generated under 2040 climate change scenarios. Model performance was consistently high (BRT: AUC = 0.99, TSS = 0.97; RF: AUC = 1, TSS = 0.99; SVM: AUC = 1, TSS = 0.99). Results revealed that T. baccata is confined to fragmented populations along Algeria’s northern mountains and coast, with the eastern Tell Atlas as its main stronghold and only marginal presence in the west. The most influential predictors were maximum temperature of the warmest month (23.5%), mean temperature of the wettest quarter (11.6%), and precipitation of the warmest quarter (9.6%). By 2040, a significant contraction of suitable habitats is projected, with refugia persisting mainly in the north-east and at higher elevations of the Hauts Plateaux. These findings highlight the high vulnerability of T. baccata to climate change in Algeria. Protecting refugial habitats and promoting restoration efforts in fragmented populations will be critical to its long-term survival. Integrating predictive modeling into conservation policy can provide a robust framework for safeguarding this relict species under future climatic uncertainty.