In the era of Industry 4.0, where automation and digitalization are central, artificial intelligence (AI) is emerging as a transformative tool across various fields, including nutrition. With an emphasis on how they might transform individualized nutrition and health optimization, this study investigates the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in nutrition science. This research systematically evaluates pertinent scientific literature from large databases using a hybrid strategy based on systematic literature review (SLR) and PRISMA criteria. Numerous AI applications in nutrition are identified by the study, including food recognition, nutritional evaluation, personalized nutrition planning, and predictive modeling for illness prevention and tracking. The chosen papers demonstrate how ML and DL methods can handle complex nutritional data, leading to more precise dietary recommendations. Future research prospects and challenges are explored, including data privacy, model interpretability, and the need for larger datasets. The results highlight AI's tremendous potential to improve public health, support evidence-based dietary recommendations, and improve individual nutritional outcomes. Researchers, legislators, and medical professionals looking to use AI in nutrition to improve health outcomes and provide more individualized dietary advice may find this thorough overview to be a useful resource.