Silicon photonic devices based on silicon waveguides are essential for high-performance, low-cost photonic integrated systems. Recent advances in data science and nanofabrication have enabled the design and manufacturing of complex silicon photonic devices with hundreds or thousands of degrees of freedom (DOF). However, conventional forward-reasoning methods are no longer suitable for designing high-performance silicon photonic devices with novel functionalities due to the complexity and non-intuitive nature of light-matter interactions. Therefore, the development of sub-wavelength silicon photonic devices that can precisely control light flow is critical and requires joint engineering and scientific efforts. This paper introduces an inverse design strategy based on heuristic and gradient descent algorithms to enable large-scale integrated device realization. It also discusses the potential of deep learning for data-driven silicon photonic design. The review provides a comprehensive overview of the challenges and prospects in this emerging field, offering guidance for scientists developing photonic integrated systems. Silicon photonic devices are highly competitive due to their ability to integrate multiple optical components on a single silicon substrate, improving chip integration. Silicon is widely used in optical communication platforms due to its excellent infrared transmission properties. Silicon photonic technology is optimal for photonic integrated circuits, integrating photonic and microelectronic devices. Silicon photonic wafers are typically fabricated using existing silicon foundries, which can be redesigned for silicon photonics. The paper explores various photonic devices, including lidar, RF photoelectric integration, coherent communication, high-speed data transmission, high-performance computing, and intelligent perception. The paper discusses two main approaches for inverse design of silicon photonic devices: optimization strategies and deep learning. Optimization strategies include direct binary search (DBS), heuristic algorithms like genetic algorithms (GA) and particle swarm optimization (PSO), and gradient-based algorithms such as the adjoint (ADJ) method, level set (LST) algorithm, and density topology optimization (DTO). These methods enable the design of complex photonic devices with high performance and efficiency. Deep learning provides a data-driven approach for silicon photonic design, enabling forward prediction and inverse design. The paper highlights the advantages of deep learning in handling high-dimensional design spaces and overcoming the "curse of dimensionality." The paper also discusses the application of these methods in various silicon photonic devices, including polarization beam splitters, power splitters, mode transformers, and optical switches. The ADJ method is particularly effective for optimizing photonic devices with high performance and efficiency. The LST method is used for topological design, while DTO is used for density-based optimization. These methods have been successfully applied to design compact, high-performance silicon photonic devices with minimal insertion loss and high port uniformity. The paper concludes that inverse design methods, including optimization and deep learning, are critical for advancing silicon photonic technology and enabling the development of complex, high-performance photonic integrated systems.Silicon photonic devices based on silicon waveguides are essential for high-performance, low-cost photonic integrated systems. Recent advances in data science and nanofabrication have enabled the design and manufacturing of complex silicon photonic devices with hundreds or thousands of degrees of freedom (DOF). However, conventional forward-reasoning methods are no longer suitable for designing high-performance silicon photonic devices with novel functionalities due to the complexity and non-intuitive nature of light-matter interactions. Therefore, the development of sub-wavelength silicon photonic devices that can precisely control light flow is critical and requires joint engineering and scientific efforts. This paper introduces an inverse design strategy based on heuristic and gradient descent algorithms to enable large-scale integrated device realization. It also discusses the potential of deep learning for data-driven silicon photonic design. The review provides a comprehensive overview of the challenges and prospects in this emerging field, offering guidance for scientists developing photonic integrated systems. Silicon photonic devices are highly competitive due to their ability to integrate multiple optical components on a single silicon substrate, improving chip integration. Silicon is widely used in optical communication platforms due to its excellent infrared transmission properties. Silicon photonic technology is optimal for photonic integrated circuits, integrating photonic and microelectronic devices. Silicon photonic wafers are typically fabricated using existing silicon foundries, which can be redesigned for silicon photonics. The paper explores various photonic devices, including lidar, RF photoelectric integration, coherent communication, high-speed data transmission, high-performance computing, and intelligent perception. The paper discusses two main approaches for inverse design of silicon photonic devices: optimization strategies and deep learning. Optimization strategies include direct binary search (DBS), heuristic algorithms like genetic algorithms (GA) and particle swarm optimization (PSO), and gradient-based algorithms such as the adjoint (ADJ) method, level set (LST) algorithm, and density topology optimization (DTO). These methods enable the design of complex photonic devices with high performance and efficiency. Deep learning provides a data-driven approach for silicon photonic design, enabling forward prediction and inverse design. The paper highlights the advantages of deep learning in handling high-dimensional design spaces and overcoming the "curse of dimensionality." The paper also discusses the application of these methods in various silicon photonic devices, including polarization beam splitters, power splitters, mode transformers, and optical switches. The ADJ method is particularly effective for optimizing photonic devices with high performance and efficiency. The LST method is used for topological design, while DTO is used for density-based optimization. These methods have been successfully applied to design compact, high-performance silicon photonic devices with minimal insertion loss and high port uniformity. The paper concludes that inverse design methods, including optimization and deep learning, are critical for advancing silicon photonic technology and enabling the development of complex, high-performance photonic integrated systems.