Global aquaculture is approaching a critical threshold where increasing production intensity is constrained by water quality deterioration, disease outbreaks, nutrient accumulation, environmental regulations, and rising operational costs. Although Recirculating Aquaculture Systems (RAS), Biofloc Technology (BFT), Artificial Intelligence (AI), and Digital Twin technologies have independently improved production efficiency, no unified theoretical framework currently explains how these subsystems can operate as a self-organizing biological-industrial ecosystem. Recent research highlights the growing importance of digital twins, AI-driven monitoring, and nutrient recycling technologies in sustainable aquaculture. This paper proposes the Self-Adaptive Nutrient Intelligence Theory (SNIT), a novel theoretical paradigm suggesting that aquaculture systems should be viewed not as fish-production facilities but as adaptive nutrient-information ecosystems. The theory introduces Nutrient Intelligence Density (NID), Ecosystem Adaptability Coefficient (EAC), and Circular Metabolic Efficiency (CME) as new governing variables. SNIT predicts that future aquaculture productivity will be determined primarily by information flow and nutrient recirculation efficiency rather than water volume or stocking density. Keywords: Aquaculture Engineering, Digital Twin Aquaculture, Biofloc Technology, Recirculating Aquaculture Systems, Artificial Intelligence, Sustainable Aquaculture, Systems Theory