Machine Learning (ML) has emerged as a transformative force in the field of Nutritional Science, revolutionizing the way we understand the intricate relationship between diet and health. This abstract provides a concise overview of the key applications of ML in Nutritional Science, emphasizing its potential to enhance dietary assessment, personalize nutrition, drive research, monitor health, ensure food safety, aid clinical practice, and inform policy decisions. ML has significantly improved dietary assessment methods by automating food recognition and consumption tracking, reducing biases associated with self-reporting. Personalized nutrition, a hallmark of ML applications, leverages genetic data to offer tailored dietary recommendations, promising to revolutionize dietary guidance and disease prevention on an individualized level. In research, ML processes vast datasets, uncovering patterns and associations between diet and health outcomes. ML-driven wearable devices enable real-time health monitoring, empowering individuals to make informed dietary choices and maintain their well-being. Furthermore, ML ensures food quality and safety through rapid pathogen detection, preventing foodborne illnesses. In clinical nutrition, ML supports healthcare practitioners by analyzing patient data, aiding in early disease detection and intervention. For nutrition policy and public health, ML contributes to evidence-based decision-making by estimating energy intake, informing policy planning and health initiatives. As ML continues to evolve in Nutritional Science, addressing ethical considerations, including data privacy and bias mitigation, becomes crucial. Interdisciplinary collaboration among nutritionists, data scientists, healthcare professionals, and policymakers is imperative to harness the full potential of ML. Together, they can promote healthier dietary habits, prevent diet-related diseases, and improve public health outcomes