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Abstract. Honeybees are important insect pollinators ensuring food security and maintaining the biodiversity of ecosystems. Monitoring honeybee behavior and beehive health status is not only essential for understanding the biology of honeybees, but also beneficial for beekeepers for beehive management. In the apiculture industry, beekeepers usually look after beehives regularly and manually. However, the assessment of beehive health status is laborious and requires considerable experience. An automated beehive monitoring system will facilitate efficient beehive management and reduce the risk of beehive losses. To address this issue, we propose an intelligent beehive health status monitoring system using multiple sensors and a sensor fusion technique. The system monitors various features of beehives, including temperature, humidity, weight, bee traffic, and acoustic signals. A long-term dataset of 4 beehives in two different locations was collected. The soundscape indices are used to interpret the acoustic signals, and these indices predict the two-class hive status with an accuracy of 0.86, which demonstrates its ability to detect the beehive health status. Regarding the aspect of weight features, the detrended weight patterns represent the daily activity pattern of a beehive, which profiles the interaction between the beehive and the environment. The maintenance of a stable in-hive temperature and humidity environment indicates the beehive‘s strength and stability. The weight and temperature features also successfully detect the two-class hive status with an accuracy of 0.82. The multi-sensor intelligent beehive monitoring system automatically collects the long-term data and detects the beehive status on a daily basis. It is an efficient tool to help beekeepers in managing their beehives in a data-driven approach thereby improving the beekeeping quality.