The current state of Russian beekeeping has been analyzed. It has been established that 95% of all enterprises consist of small-scale private and farm operations that rely on outdated technologies and a high degree of manual labor. Based on the conducted research, the technological level of beekeeping corresponds to the second and third technological paradigms. In the context of implementing the Agriculture 4.0 concept, beekeeping requires transformation through the integration of artificial intelligence. A review of existing digital solutions highlights the lack of a comprehensive approach to digitalization within the industry. The initial stage of building a digital infrastructure involves computerization through technical tools for collecting, storing, and transmitting information. Given that one of the key challenges is monitoring bee populations within hives, the use of in-hive sensors and IoT devices allows beekeepers to gather real-time data on microclimate conditions, colony strength, overall hive health, and honey productivity—enabling them to make timely and informed decisions. Artificial intelligence and machine learning can be applied to analyze collected data, identify patterns and trends, and help beekeepers prevent issues while optimizing hive management. Machine vision systems can process images captured by sensors to assess bee health, monitor behavior, and detect diseases and pests. Additionally, satellite imagery can be used to determine the blooming periods of entomophilous agricultural crops, ensuring the timely deployment of bees for pollination. The study identifies key challenges hindering digitalization in the beekeeping sector. It has been established that the development of digital resources is constrained by a lack of necessary financial resources among beekeepers, an underdeveloped information and communication network, the absence of widespread broadband internet access, a lack of experience in digital technologies, insufficient knowledge among specialists, and weak motivation. The study concludes that there is a critical need for a unified platform for data processing, storage, and decision-making based on collected information.