Autonomous perfusion systems powered by machine learning represent a transformative advancement in organ preservation and tissue engineering. By integrating real-time sensor data with adaptive AI algorithms—such as reinforcement learning, Gaussian processes, and predictive modeling—these systems dynamically adjust perfusion parameters to optimize oxygenation, nutrient delivery, and waste removal. This closed-loop control enables precise, consistent, and prolonged ex vivo organ maintenance, reducing manual oversight and enhancing viability, especially for marginal grafts. From microfluidic organ-on-chip platforms to large-scale bioreactors, the convergence of AI and perfusion technology promises to revolutionize both research and clinical transplantation.