Algorithms and technologies of geolocation digitalization and optimization of apiary location present a vital task in the field of efficient apiary management and the increase in honey yield productivity. The goal of the present research is to develop methodology, which would allow to use geoinformational technologies, algorithms for the analysis of nectar-bearing lands and automated systems for precise planning of location for bee colonies. The methodology includes the use of satellite monitoring, data from unmanned aerial vehicles and climate-and-physical analysis for the assessment of potential productivity of land areas. The research suggests to use algorithms to calculate the optimum number of bee colonies and automated models to forecast honey yield, which facilitate more efficient management of apiary resources. The results show that the developed algorithms and digital tools can be successfully applied to optimize the logistics of migratory beekeeping, reduce transportation costs and increase the overall productivity of beehives. The use of geoinformational systems and machine learning algorithms enables the systematic selection of optimal locations for apiaries and enhances the accuracy of honey yield forecasts. The suggested methodology can become a basis for new standards in digital management of beekeeping, ensuring adaptivity to climatic changes and individual features of regional nectar-bearing lands. The developed technologies are promising for integration into digital services and software products, aimed at automation of apiary monitoring, forecasting honey yield and management of migratory beekeeping processes. Such approach opens up new opportunities for investigations in the field of agricultural digitalization and increase of beekeeping productivity. Moreover, the suggested algorithms can be used for subsequent development of intelligent decision-support systems, which provide comprehensive analysis of geolocation and climatic factors for sustainable growth of beekeeping.