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Photonic devices based on silicon waveguides are essential to versatile high‐performance and low‐cost photonic integrated systems. Extremely complex silicon photonic devices with hundreds or even thousands of degrees of freedom (DOF) are successfully designed and manufactured based on recent advances in data science and nanofabrication technology. At this level, conventional forward‐reasoning may no longer be suitable for designing high‐performance silicon photonic devices with novel functionalities since the light‐matter interaction is complex and non‐intuitive. Therefore, the timely development of sub‐wavelength silicon photonic devices that can precisely mold the flow of light is a critical and urgent issue requiring joint engineering and scientific efforts. In this paper, an inverse design strategy based on heuristic and gradient descendant algorithms, enabling the realization of large‐scale integrated devices is first introduced. Subsequently, the burgeoning deep learning technology, which offers a promising direction for the automation design of silicon photonics with a data‐driven approach, is discussed. Finally, the obstacles and prospects in this emerging research direction are revealed. Detail discussions from multiple perspectives are provided. This review aims to provide general guidance and a comprehensive reference for scientists developing photonic integrated systems. [ABSTRACT FROM AUTHOR]