Nonlinear optical crystals are the core devices of all-solid-state lasers, and have extensive and important applications in information technology and national security. With the development of high-performance computing, the “top-down” computer-aided design methods have gradually become an important part of nonlinear optical materials design. In addition, the large-scale structural and properties information obtained based on high-throughput computing provides a solid data foundation for data mining and machine learning algorithm training, accelerating the development of the fourth paradigm of material design. This paper starts with the computational design of nonlinear optical materials, and then, discusses the new paradigm of data-driven nonlinear optical materials theoretical design. Finally, the recent research progress of our team in high-throughput screening, crystal structure prediction, and machine learning accelerated nonlinear optical materials are reviewed.