Adapting agroforestry systems to future climate conditions requires robust predictive tools. While process-based models are limited to a narrow range of species, Species Distribution Modelling (SDM) offers a scalable and effective method for assessing the potential impacts of climate change on a wide array of tree species. SDM employs statistical inference to estimate environmental niches for species, varieties, or seed sources, enabling the generation of distribution maps across environmental and geographic spaces. Recent advancements in machine learning and access to high-resolution environmental data have significantly enhanced SDM capabilities. Ensemble modelling techniques—drawing from algorithms such as MaxEnt, boosted regression trees, and random forests—improve prediction accuracy through weighted averaging. However, the flexibility in assigning algorithm weights introduces variability. To address this, we developed a novel statistical method for weight tuning and a suitability mapping strategy based on presence–absence predictions across multiple algorithms. These innovations are implemented in the open-source BioversityR package, with outputs compatible with platforms like Google Earth for enhanced visualization and dissemination. Complementary use of the climate analogue method offers an additional layer of interpretability, as illustrated in case studies from Africa, Asia, and Latin America. Specific examples include projections for timber species in Latin America, food trees in Burkina Faso, and mango variety transitions in Kenya, where production data were integrated into ensemble mapping. Furthermore, integrating SDM with potential natural vegetation data enhances seed sourcing strategies, ensuring they are more resilient to climatic shifts. These approaches collectively support informed decision-making for climate-resilient agroforestry development.