Accurate understanding and prediction of melt-pool dynamics are crucial for Additive Manufacturing (AM) processes, as they significantly impact final products’ quality and mechanical properties. In this paper, we propose a novel method, Melt-pool Generative Pre-trained Transformer (MeltpoolGPT), for predicting the next melt pools using a GPT-based architecture specifically designed for video frame predictions. Our approach captures spatio-temporal dependencies between melt pools and learns the underlying physical dynamics governing laser-powder-bed-fusion processes. We evaluate MeltpoolGPT on melt-pool image data acquired from the AM Metrology Testbed at the National Institute of Standards and Technology, and design a series of experiments to assess the accuracy and stability of its predictions. Our experiments indicate that MeltpoolGPT achieves high accuracy in predicting the next melt-pool images, outperforming other state-of-the-art methods. This work presents a first step in predicting future melt pools, a largely unexplored area with immense potential to benefit real-time monitoring and control in AM significantly.