Noncommunicable chronic illnesses as diabetes, obesity, and cardiovascular disorders, are a deep-seated issue in the world health of the population, and the traditional nutritional approaches do not reflect individual variation. In this regard, the current paper summarises the current advances in artificial-intelligence-guided robotics to precision nutrition, explaining the functional role of these systems in personalising dietary plans to genomic, metabolomic, and lifestyle factors, which contribute to the improvement of chronic disease treatment and nutritional education. The research synthesises the evidence based on a multidisciplinary body of literature, which provides the outline of the main technologies that make this paradigm shift possible. Convolutional neural networks are used such as real-time diet tracking, and collaborative robotic platforms are used, with the help of which meals are prepared automatically. All these innovations contribute to the increased personalization in real-time and compliance with the prescribed dietary guidelines. Clinical validity of these technologies has been indicated in empirical studies that have shown the technologies to reduce glycaemic variability and cardiovascular risk indicators. In addition to this, educational modules incorporated into these systems and scalable have significant positive impacts on nutritional literacy among various demographic groups. At the same time, ethical issues like interoperability difficulties and access limitations are examined. It also projects the future trends, i.e., explainable artificial intelligence architectures and hybrid blockchain solutions that can resolve these obstacles. These developments are indicative of structural change towards equitable and sustainable health-care systems that are in tandem with the sustainable development goals of universal well-being. The use of AI-robotics in precision nutrition thus plays a critical role in the creation of significant public health benefits and should be met with a focused policy and scientific intervention to overcome the current challenges and enable its wide implementation.