Snapshot spectral imaging (SSI) captures complete spectral information of a scene in a single "snapshot," enabling real-time dynamic observations. It has wide applications in fields like environmental monitoring, medical diagnostics, and industrial inspection. SSI systems aim to achieve high spatial and spectral resolution with compact form factors and computational efficiency. Traditional methods, such as tunable filters and scanning, face challenges in real-time scenarios due to the "time-multiplexing dilemma." Recent advancements include computational reconstruction methods and non-computational optical engineering. Metasurface technologies offer unprecedented control over optical properties at sub-wavelength scales, enabling more compact, efficient, and versatile SSI systems. This review systematically presents SSI techniques, focusing on spatial-spectral mapping and metasurface-based imaging. It categorizes existing techniques into non-computational spatial-spectral mapping, computationally required coded reconstruction, and metasurface imaging. The review highlights key challenges and future directions, emphasizing the potential of metasurface technologies in next-generation SSI. Integral field snapshot spectral imaging uses optical components like slicer mirrors, lenslet arrays, and optical fiber bundles to capture spectral data. Spatial replication techniques use lenslet arrays or beam splitters for parallel spatial operations. Coded reconfiguration spectral imaging encodes light's amplitude, phase, or wavelength using specially designed optics, enabling spectral reconstruction. Pixelated filter arrays and diffractive optical elements (DOEs) are also used for spectral imaging. Diffraction modulation techniques, including diffractive diffusers and lenses, enhance spectral resolution and imaging performance. Combined modulation techniques improve flexibility and spatial-spectral information multiplexing. Spectral reconstruction technologies rely on compressed sensing and optimization algorithms to recover spectral data from measurements. This review provides a comprehensive overview of SSI technologies, their principles, applications, and future research directions.Snapshot spectral imaging (SSI) captures complete spectral information of a scene in a single "snapshot," enabling real-time dynamic observations. It has wide applications in fields like environmental monitoring, medical diagnostics, and industrial inspection. SSI systems aim to achieve high spatial and spectral resolution with compact form factors and computational efficiency. Traditional methods, such as tunable filters and scanning, face challenges in real-time scenarios due to the "time-multiplexing dilemma." Recent advancements include computational reconstruction methods and non-computational optical engineering. Metasurface technologies offer unprecedented control over optical properties at sub-wavelength scales, enabling more compact, efficient, and versatile SSI systems. This review systematically presents SSI techniques, focusing on spatial-spectral mapping and metasurface-based imaging. It categorizes existing techniques into non-computational spatial-spectral mapping, computationally required coded reconstruction, and metasurface imaging. The review highlights key challenges and future directions, emphasizing the potential of metasurface technologies in next-generation SSI. Integral field snapshot spectral imaging uses optical components like slicer mirrors, lenslet arrays, and optical fiber bundles to capture spectral data. Spatial replication techniques use lenslet arrays or beam splitters for parallel spatial operations. Coded reconfiguration spectral imaging encodes light's amplitude, phase, or wavelength using specially designed optics, enabling spectral reconstruction. Pixelated filter arrays and diffractive optical elements (DOEs) are also used for spectral imaging. Diffraction modulation techniques, including diffractive diffusers and lenses, enhance spectral resolution and imaging performance. Combined modulation techniques improve flexibility and spatial-spectral information multiplexing. Spectral reconstruction technologies rely on compressed sensing and optimization algorithms to recover spectral data from measurements. This review provides a comprehensive overview of SSI technologies, their principles, applications, and future research directions.