Most developing economies continue to be overdependent on agriculture, yet farmers fail to access professionalknowledge and information in a timely fashion that may help them generate higher yields, reduce risks, and makedecisions. In the past, farmer-advising systems have relied on face-to-face meetings, call centers, or fixed platformsin relaying information. Another revolutionary solution is the appearance of artificial intelligence that can be used inthe form of multilingual chatbots with the help of Natural Language Processing (NLP). These chatbots provide farmerswith highly personalized, context-sensitive, and readily available consulting services in their own languages. Thisstudy explores the concept, design, and effectiveness of multilingual chatbots in terms of advising farmers usingnatural language processing. It reviews the existing studies, discusses methods of chatbot development, analyzeschallenges, including a variety of dialects and low-resource languages, and presents potential conclusions in thecontext of usability, functionality, and socioeconomic consequences. To ensure trust, scalability, and inclusivity, thediscussion ends with a glimpse of the future and the need to focus on explainable AI, advanced NLP models, andinteraction with Internet of Things (IoT) devices..