Personalized nutrition has the potential to revolutionize health by integrating biomarkers and artificial intelligence (AI) to provide tailored dietary recommendations. This systematic review synthesizes existing research on the role of biomarkers and AI in personalized nutrition, focusing on the critical need for a robust computational infrastructure. A comprehensive search of major databases, including PubMed, Scopus, and Web of Science, was conducted to identify studies published between 2010 and 2024. A total of 50 studies were selected based on their relevance and contribution to the understanding of how biomarkers and AI enhance the accuracy of nutritional assessments and recommendations. The review found that biomarkers play a crucial role in detecting molecular and biochemical changes linked to nutrient intake and metabolism, providing a precise assessment of nutritional status. Concurrently, AI technologies analyze large datasets—encompassing genetic, dietary, and health data—to generate personalized recommendations that account for individual variability. This combination offers significant advantages over traditional population-based studies. However, the review highlights the persistent challenges in building a robust computational infrastructure necessary to support these innovations. Effective infrastructure must address the complexity of dietary databases, enabling the accurate translation of food components into nutrients and energy. Additionally, the development of bioinformatics frameworks could standardize and annotate nutritional data, facilitating better dietary monitoring and management. This review concludes that while biomarkers and AI are transforming personalized nutrition, advancing computational infrastructure is essential to fully realize their potential and improve the quality and accessibility of personalized dietary guidance. Further research is recommended to overcome the current limitations and ensure the ethical and effective implementation of these technologies.