Vietnam’s agricultural sector faces increasing challenges due to unpredictable weather patterns and limited water resources, necessitating efficient and sustainable irrigation management. While existing smart irrigation systems offer essential functionalities, their limited usability–due to complex interfaces and lack of hands-free control–hinders adoption among farmers. This study presents a novel smart irrigation management system integrating a voice- and text-based chatbot powered by a large language model (LLM). The system enables seamless, hands-free operation, improving accessibility and user engagement. Experimental evaluations demonstrate that our LLM adapts effectively to regional Vietnamese dialects, achieving \(82\%\) accuracy on agricultural slang - \(17\%\) higher than commercial alternatives. Additionally, after testing30samples, the LLM correctly executed27function calls, achieving a \(90\%\) accuracy rate in translating user intents into appropriate irrigation commands. Performance benchmarks show efficient system responsiveness, with an average function call latency of150ms, database queries at50ms, and real-time sensor readings within80ms. These results confirm that the system can deliver timely and reliable feedback for irrigation management. By enhancing usability and real-time decision-making, this research contributes to the broader adoption of smart irrigation technologies in Vietnam, promoting sustainable water resource management and improving agricultural productivity despite pressing environmental challenges.