Wasteful resource use, degraded soil, and significant post-harvest losses are just a few of the grave sustainability challenges South Asian agriculture presents that endanger the UN Sustainable Development Goal (SDG) 12: Responsible Consumption and Production. A subset of artificial intelligence (AI), Retrieval-Augmented Generation (RAG) systems have revolutionary potential for providing practical, regionally relevant agricultural recommendations. This paper develops a RAG-based advisory system especially meant for smallholder farmers in South Asia by combining indigenous agricultural knowledge with scientific literature from AGRIS. The system uses advanced language models like Falcon-7B-Instruct and Llama-3.2-3B-Instruct to generate context-aware responses on post-harvest loss reduction, water optimisation, and soil fertility management. Although farmers’ qualitative assessments highlight usability and practical effect, quantitative studies using BLEU-4 and ROUGE-L metrics reveal great accuracy and relevance. Sustainability forecasts indicate that post-harvest losses (18%) and chemical fertiliser use (22%) will much decline, which fits SDG 12 objectives. Additionally, information is provided taking sustainability as an important factor. This study addresses significant voids in AI-driven agriculture and paves the way for scalable solutions that support sustainable farming practices by including indigenous knowledge, emphasising regional specificity, and maximising deployment for low-resource settings.