In this work, a thorough strategy for solving the problem of skin disorders in cattle is offered that uses state-of-the-art deep learning methods and interactive artificial intelligence applications. CNN and Vision Transformer’s deep learning LLM models are used to create a superior cow skin disease classifier. A wide dataset of more than 2000 photos is collected, including a number of skin diseases such as ringworm, eye disease, papillomatosis, photosensation, catarrhal tongue fever, and lumpy skin disease, using careful data collection and augmentation procedures. Innovative data enrichment techniques have increased the diversity and richness of the dataset. With the help of this improved data set, we were able to train the proposed deep learning model and obtained a staggering 85% accuracy rate. A model was implemented using Hugging Face AI models to ensure wider availability and usability, guarantee smooth integration and user-friendly interactions. In addition, the work was continued on developing an engaging AI chatbot designed specifically to talk about cattle skin diseases. This chatbot has features including chat history visualization, text-to-speech capabilities, and download transcripts of discussions as PDF documents. In the field of veterinary science, incorporating these aspects not only improves the user experience, but also promotes collaborative learning and information distribution.