This research presents an IoT-enabled smart aquaponics system integrating multi-modal sensor networks with advanced machine learning algorithms to achieve autonomous optimization of symbiotic crop–fish production environments. The system employs a distributed sensor architecture incorporating pH, dissolved oxygen, ammonia, nitrate, temperature, humidity, light intensity and turbidity sensors, interfaced through ESP32-based edge computing nodes with real-time data transmission capabilities via LoRaWAN and WiFi protocols. The core innovation lies in the implementation of a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) for image-based plant health assessment, Long Short-Term Memory (LSTM) networks for temporal pattern recognition in water quality parameters, and Reinforcement Learning (RL) agents for dynamic system optimization. The AI model processes over 15,000 data points hourly, enabling predictive analytics for disease detection, nutrient deficiency identification and growth trajectory forecasting with 94.7% accuracy. Advanced computer vision algorithms utilizing YOLOv8 architecture perform real-time fish behavior analysis and biomass estimation, while hyperspectral imaging integrated with transformer-based attention mechanisms monitors plant stress indicators at cellular resolution. The system's autonomous control mechanisms regulate LED spectrum optimization (380–780 nm), nutrient dosing through precision peristaltic pumps, water circulation via variable-speed pumps and climate control through HVAC integration. Experimental validation demonstrates a 43% increased crop yield, 28% enhanced fish growth rates and a 35% reduction in water consumption compared to conventional systems. The platform achieved 99.2% uptime with sub-second response times for critical parameter adjustments. Machine learning models successfully predicted system failures 72 hours in advance, enabling proactive maintenance protocols. The system's scalability is demonstrated through blockchain-based data integrity verification, edge-to-cloud hybrid processing architecture, and standardized API interfaces enabling seamless integration with existing agricultural management systems. This breakthrough technology represents a paradigm shift toward sustainable, intelligent food production systems capable of addressing global food security challenges while minimizing environmental impact.