The increasing demand for high-quality, nutritious food necessitates innovative approaches to food quality and nutrition assessment. This paper explores the application of machine learning techniques in predictive modeling for assessing food quality and nutrition. Various machine learning models, including regression models, classification models, neural networks, and ensemble methods, are employed to predict nutritional content, evaluate quality parameters, and monitor food safety. Case studies demonstrate the effectiveness of these models in real-world scenarios, highlighting their potential to enhance food production processes and ensure consumer safety. The findings suggest that machine learning offers a robust framework for advancing food quality assessment, providing accurate, real-time predictions that can significantly improve industry standards.