One of the endearing insects that use group behaviour to accomplish strong actions is the honey bee. It is also crucial for humanity to protect honey bees in order to maintain the ecological balance. A lot of effort is needed to track and identify the various bee species during the course of their lives. To protect the different species of honey bees from harm, automated species classification is essential. In order to prevent the spread of diseases to other healthy honey bees, it is necessary to discover the diseases that afflict honey bees on their own during their lifetime. The suggested method seeks to discover the diseases to which honey bees are susceptible. The proposed study uses a 2D convolutional neural network (2D-CNN) as a classifier. Furthermore, sophisticated data augmentation methods including scaling, translation, rotation, and reflection are used to improve the gathered dataset. Over five thousand photos of bees with various type characteristics are used for purposes of learning. The suggested model achieved 94% accuracy in identifying the health of bees.