Plant care techniques in India are frequently hampered by the absence of organized, reliable information sources. Farmers and gardeners usually depend on disjointed information from dubious internet sources, unofficial community counsel, or local retailers. This leads to improper application of fertilizers or pesticides, uneven plant care practices, and incorrect plant disease diagnosis. Nowadays, due to fragmented knowledge and the absence of trusted and reliable sources, plant care is very difficult. Also, language barrier plays another major role as people in India speak a variety of languages but the AI-powered assistants or any other systems are majorly built to converse in English. Therefore, these systems are of no use to India’s local population. To address these issues here in this research a weather-based plant disease detection and recommendation system along with multilingual chatbot based on RAG and LLM is proposed. Here a convolutional neural network is implemented to analyze plant images and predict diseases, which is then integrated with recommendation system to recommend the watering of plants according to soil moisture and it also predicts the weather for the next few days. Another key module in this plant care system is the multilingual chatbot which makes use of Helinski model for language translation and involves RAG information retrieval integrated with LLAMA-2.0 model to efficiently answer user’s queries regarding plant care. By seamlessly integrating weather data into disease diagnosis and utilizing state-of-the-art AI models for user interaction, this system offers a comprehensive and intelligent approach to plant health management, ensuring optimal growth and disease prevention.