The intersection of biological systems and artificial intelligence (AI) introduces transformative opportunities for computing architectures that transcend traditional paradigms by integrating adaptability, self-optimization, and environmental learning capabilities. This study proposes the Bio-IA Supercomputer, a pioneering hybrid computational framework that synergizes DNA-based molecular logic, living cellular circuits, and advanced AI algorithms to deliver unprecedented levels of parallelism combined with minimal energy consumption. The system’s core innovation lies in its capacity for autonomous biochemical error correction through real-time reinforcement learning and biofeedback mechanisms, thereby bridging the gap between static silicon-based hardware and the dynamic complexity of biological matter. Experimental validation demonstrates that the platform can execute complex computational tasks with scalable modularity, suggesting broad applicability in precision medical diagnostics, responsive drug delivery systems, large-scale combinatorial optimizations, and the engineering of adaptive synthetic biological networks. This work offers a novel paradigm for the coalescence of bioinformatics, synthetic biology, and machine learning, forging a path toward the next generation of intelligent biocomputing devices. Keywords Biocomputer; Artificial Intelligence; DNA Computing; Cellular Logic; Hybrid Systems; Microfluidics; Bioinformatics; Reinforcement Learning; Biological Neural Networks.