Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and improving climate resilience. Yet, its widespread adoption among farmers remains limited due to socio-economic, infrastructural, and justice-related chal-lenges. This study aims to investigate the causal configurations influencing AI adoption in NCAM, using an integrated framework that combines the Technology Acceptance Model (TAM) with a justice-centred approach and ecological values. A mixed-methods design is employed, applying fuzzy-set Qualitative Comparative Analysis (fsQCA) to farmer survey data and critical discourse analysis to qualitative narratives. The findings reveal that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without support-ive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, providing a more holistic understanding of technology acceptance in sustainable agricul-ture. By bridging discourse analysis and fsQCA, this research highlights the need for jus-tice-centred AI solutions tailored to diverse farming contexts. The study contributes to ad-vancing sustainable agriculture, technology inclusion, and resilience, thereby supporting the United Nations Sustainable Development Goals (SDGs).