The convergence of machine learning and ultrafast photonics cutting-edge crossover technologies in the context of artificial intelligence takes an unconventional approach to provide an unparalleled photonic perspective. This intersection of computer science, photonics, and materials platforms will enable new approaches to the large-scale photonic design of unique functions as well as optical characterization, laying the cornerstone for efficient energy conversion systems. We envision that a global optimization framework based on a multi-step machine learning strategy can build a more general intelligent ultrafast photonic system, where the first step can be to define the main target function of the device and determine the appropriate photonic concept to provide the best performance. The second step is to select a suitable material platform and build an extensive database of optical materials. By using the selected material properties, an optimized design solution for the material device can be provided. The third step is to determine the appropriate fabrication conditions (growth conditions, doping levels, stoichiometry, etc.) and integration schemes. The interplay between new photonic structures and machine learning may overcome the limitations of current computational methods and systems, provide unparalleled capabilities in light-matter interactions and unlock new device concepts, and may lead ultrafast photonics research to new frontiers that could usher in a brighter era of artificial intelligence.