Aquaculture is expected to account for two-thirds of global fish consumption by 2030, highlighting the need for sustainable and efficient practices. Feeding is crucial to aquaculture success, influenced by factors like fish size, environment, and health. This study addresses a gap in feeding control for sea cages by developing a real-time monitoring system, using AI models and computer vision to analyze feeding behavior with European sea bass as pilot species. Key metrics like fish speed and a new feeding behavior index (FBI) were used to assess responses to different feeding scenarios. The results revealed distinct behavior patterns based on feeding quantity, with imbalances in activity when fish are overfed or underfed. The results can be used for predicting the level of satiation of the fish and controlling feeding duration.