Abstract: Intelligent feeding devices constitute pivotal technological equipment driving the transformation of aquaculture toward intelligent and intensive operational paradigms. The fundamental mechanism centers on acquiring comprehensive environmental and aquaculture organism information through sophisticated multimodal perception systems, integrating advanced data modeling and decision-making algorithms for analytical processing, and implementing precision execution protocols to achieve highly efficient feeding operations. This comprehensive study systematically examines the developmental trajectory of intelligent feeding devices and establishes a robust system framework comprising three interconnected architectural layers: perception layer, decision-making layer, and execution layer. The perception layer focuses primarily on comprehensive monitoring of water quality parameters, meteorological conditions, and aquaculture organisms, while facilitating sophisticated multi-source information fusion processes. This layer incorporates advanced sensor networks including dissolved oxygen monitors, temperature sensors, pH meters, turbidity analyzers, underwater imaging systems, acoustic monitoring devices, and biometric measurement instruments. These integrated sensing technologies enable continuous real-time data acquisition regarding environmental fluctuations, fish behavioral patterns, growth performance indicators, and feeding response characteristics, providing essential foundational data for higher-level analytical processes. The decision-making layer operates as the cognitive nucleus of the system, utilizing sophisticated growth models and optimization algorithms to generate scientifically-based and individually-customized feeding strategies. This layer employs advanced computational methodologies including machine learning algorithms, artificial neural networks, fuzzy logic controllers, genetic algorithms, and predictive modeling frameworks to analyze complex multi-dimensional datasets. The system processes information regarding species-specific nutritional requirements, growth kinetics, environmental conditions, feeding histories, and market demands to determine optimal feeding schedules, appropriate feed quantities, nutritional compositions, and distribution timing patterns. The execution layer implements physical operational capabilities through systematic improvements in device adaptability, sophisticated feed transportation mechanisms, and optimized distribution pattern designs, thereby enhancing overall system stability and broad applicability across diverse aquaculture environments. This layer encompasses precision mechanical engineering components, automated conveyor systems, programmable dispensing units, variable-speed distribution mechanisms, and intelligent control interfaces that ensure accurate feed delivery while maintaining consistent performance under varying operational conditions and environmental challenges. Current research findings indicate that existing technological implementations continue to confront significant challenges that constrain optimal performance and widespread adoption. These limitations include insufficient precision in multi-source data fusion processes, resulting in potential information loss and reduced analytical accuracy; limited generalization capabilities of existing computational models, restricting their adaptability across diverse aquaculture species, environmental conditions, and operational scales; and inadequate device reliability under harsh marine operational environments, leading to maintenance challenges, operational disruptions, and increased lifecycle costs. The research presented in this study provides not only a comprehensive systematic framework for the design and optimization of intelligent feeding systems but also demonstrates substantial practical value in enhancing feed utilization efficiency, reducing operational costs, and promoting environmentally sustainable aquaculture practices. The proposed framework offers theoretical guidance for system integration, performance evaluation, and technological advancement while addressing critical industry needs for improved productivity, cost-effectiveness, and environmental stewardship. Future research directions should prioritize strengthening interdisciplinary collaboration among marine biology, computer science, mechanical engineering, materials science, and environmental science disciplines, while emphasizing rigorous practical application validation through extensive field testing and performance evaluation under real-world operational conditions. Research efforts must focus on advancing algorithm robustness through sophisticated machine learning techniques, improving device performance through innovative engineering solutions, and developing standardized testing protocols for comprehensive system evaluation. These strategic initiatives will facilitate large-scale commercial deployment of intelligent feeding technologies within aquaculture operations and accelerate comprehensive intelligent transformation of the global aquaculture industry, ultimately contributing to enhanced food security, environmental sustainability, and economic viability of modern aquaculture systems.