Lot-sizing Problems aim to identify optimal production periods and quantities to meet demand while minimizing costs related to production, setup, and inventory. This article explores how Artificial Intelligence (AI) is transforming how the Lot-Sizing Problem is approached in real-world scenarios. Traditional methods face challenges in optimizing production cycles due to their complexity or lack of quality in their solution. Leveraging AI, including Neural Networks, Genetic Algorithms, Deep Learning, and others, offers superior problem-solving capabilities. This paper focuses on evolution of the literature of Artificial Intelligence techniques applied to Lot-Sizing Problems. The aim of the review is twofold. First it provides an insight on the field to highlight the many ways AI can be applied in Lot-Sizing and second, it goes deeper on a specific case study on an AI technique, the ANN. This paper contributes to AI’s application in Lot-Sizing, emphasizing its role in optimizing production, enhancing decision-making, and addressing contemporary challenges. The findings underscore the importance of integrating AI technologies to navigate evolving complexities in production planning.