<p>The post-silicon era demands biosensors, where intelligence is built into the material itself. This review delves into how artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), is reshaping aptamer-based organic field-effect transistors (OFETs) by predicting molecular interactions, optimizing semiconductor properties, and designing novel aptamers in-silico. Generative-AI, reinforcement learning (RL), and digital twins are coming together to co-design extremely efficient and accurate self-learning adaptive biosensors. Moreover, architectures combined with AI are opening the way for closed-loop systems, neuromorphic sensing, and multi-modal platforms able to perform smart signal correction, baseline adaptability, and environmental responsiveness. This review presents AI as a catalyst for the design revolution rather than just a computational tool, laying out a new multidisciplinary roadmap, where materials science, bioengineering, and AI work together to define the future of biosensing and discuss challenges, such as data scarcity, experimental validation and the ethical, explainable use of AI in diagnostics. In the end, our work establishes a new age of AI-biosensor symbiosis where the material itself generates sensing intelligence.</p>