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The convergence of artificial intelligence and the Internet of Things is creating new possibilities for adaptive farming systems. In agricultural settings, this convergence allows sensor networks, machine learning models, communication infrastructures, and automated decision support tools to operate as interconnected cyber-physical systems. Yet many current smart farming architectures remain technically fragmented and do not fully connect operational decisions with sustainability feedback. A central problem is that sensing, intelligence, agronomic reasoning, and sustainability assessment are often treated as separate system layers. Sensor data may support monitoring, machine learning may support prediction, and sustainability indicators may be reported after implementation, but these functions are rarely organised as a continuous adaptive loop. This separation limits the ability of farming systems to learn from environmental consequences and adjust decisions in real time. This article proposes a conceptual architecture for AIoT-enabled adaptive farming systems. The architecture integrates distributed sensor networks, edge-based intelligence, agronomic knowledge models, and sustainability feedback mechanisms into a closed-loop system. It is designed for farming environments where decisions must be locally responsive, scientifically grounded, and aligned with long-term resource stewardship. The article is based on a conceptual design synthesis of peer-reviewed literature published. The synthesis draws on agricultural IoT, edge computing, machine learning, crop monitoring, remote sensing, smart farming platforms, and sustainability-oriented digital agriculture. No new empirical data are presented. The resulting architecture specifies four interacting layers: sensing, edge intelligence, agronomic knowledge integration, and sustainability feedback. It defines how sensor observations move through edge analytics and knowledge-informed decision support before being evaluated against sustainability indicators that feed back into future control actions. The article contributes a systems blueprint for adaptive farming architectures that are not only intelligent and data-driven but also environmentally responsive and sustainability-oriented.