The enhancement of efficiency and productivity is essential for companies in the industry. The use of Artificial Intelligence has become a vital strategy to achieve these objectives, enabling process optimization and driving results. This article examines the influence of Artificial Intelligence applications on Production Planning and Scheduling. The applications studied were Machine Learning techniques, Artificial Neural Networks and Deep Learning. It begins with a literature review of these three techniques, followed by a bibliometric analysis and a detailed review of case studies that emphasize the respective applications. The bibliometric analysis highlighted the recent growth of this research area, as well as the interconnectedness of the topics that characterize it. While Machine Learning algorithms have broad applications across various domains, they stand out for their ability to positively impact inventory management policies by enabling real-time adjustments. In a different vein, Artificial Neural Networks are used to predict quality, control production, and estimate productivity. Meanwhile, Deep Learning develops globally optimal solutions regarding production scheduling problems, enhances data processing, and makes real-time decisions. In the future, the use of Artificial Intelligence techniques is expected to rely less on human intervention, providing increasingly reliable and timely results. The capabilities offered by these techniques will allow for a swift and dynamic management of tasks such as defining maintenance schedules, machine availability and work orders to perform. In general, it can be concluded that the use of Artificial Intelligence in this domain has a positive effect, with substantial improvements in the quality of production forecasts.