Current personalized nutrition systems encounter challenges in capturing individual preferences and health goals, impeding optimal dietary decision-making. This study introduces a novel hybrid recommender system that merges a robust user feedback loop with advanced machine learning techniques. Through the integration of transfer learning, counterfactual reasoning, and meta-learning, the system exhibits significantly enhanced performance compared to conventional approaches. In contrast to the baseline, our hybrid system demonstrates a 7.0% increase in overall recommendation accuracy, an 11.3% improvement in health goal recall, and a 6.7% boost in food recommendation recall. Furthermore, it showcases a 3.0% rise in health goal precision, a 2.9% increase in food recommendation precision, and a notable 102.2% surge in feedback integration efficiency. Moreover, user satisfaction experiences a notable 15.4% elevation. This dynamic system empowers individuals to make informed dietary choices via personalized recommendations and continual feedback-driven refinement, heralding a more effective and engaging era in personalized nutrition.