This research proposed and built the first integrated AI-based honeybee health assessment system called BeeMind AI. It includes eight different sensors, allowing it to track optimal in-hive conditions as well as the overall health and population of the honeybees. BeeMind AI has many diverse applications due to its ability to analyze honeybee movement and behavioral patterns to determine honeybee health, and it was used to evaluate the effects of different nutrients on honeybee health in two experiments, one in a dual-chambered maze, and another in a free-flying paradigm. The free-flying experiment was conducted to study the effect of nutrients on return rates of honeybees at distances of 300 m, 500 m, and 800 m. Additionally, a new method of individually feeding bees nutrients was developed. Through AI video analysis, the results from the free-flying experiment indicate that honeybees are very capable foragers, with the return rates of the control group even at 800 m being close to 75%. It was found for the first time that C60 nanoparticles have significant positive effects on learning and memory, improving return rates by around 9% at 300 m, 16% at 500 m, and 20% at 800 m, while neonicotinoid pesticides have negative effects on return rates, reducing them significantly by up to 30% and confirming the results from the dual-chambered maze experiments. A statistical t-test method was used for data analysis. This research is a significant step forward in understanding the diverse impacts of different nutrients on honeybee health and behavior.