Bees and beekeeping are increasingly recognised as important contributors to sustainable development. Beekeeping is a landscape-scale process that involves complex interconnectivities between beekeepers, beehives and bee forage. Yet, accounting for these interactions within beekeeping system models is challenging as interactions are dynamic and often influenced by human behaviour and decision making. To this end, this research describes a spatially explicit modelling approach B-Agent, which draws upon multiple stakeholder engagement, a machine-learning algorithm and an agent-based model to simulate beehive migration processes. The Western Australian beekeeping sector provides a case study for model development and testing, to examine changes in (i) distances travelled by beekeepers, (ii) the frequency of beehive migration, and (iii) the spatial distribution of harvest locations resulting from climate related impacts on forage availability. The results indicate an increase in travel distance and frequency of hive migrations for commercial beekeepers under a moderate emissions climate scenario and indicated an eastward shift in the spatial distribution of harvest locations. The approach provides an evidence-base for better-informed management decisions in order to improve the long-term sustainability of beekeeping systems in Western Australia and beyond.