Raman spectroscopy is a non-elastic light scattering, non-destructive spectroscopic detection method based on the interaction between laser and matter. It finds applications in modern battlefields, industrial production, social security, and the detection of hazardous or prohibited goods. Compared to visible light and near-infrared Raman spectroscopy, solar-blind ultraviolet (UV) Raman spectroscopy offers advantages such as reduced environmental interference, higher scattering intensity, and safety for human eyes. These characteristics make it suitable for detecting explosive substances and enabling remote sensing of samples in natural environments. However, solar-blind UV Raman spectroscopy faces several challenges that affect qualitative and quantitative analyses: 1) at equivalent spectral resolution, the Raman shift (wavenumber) resolution of UV Raman spectroscopy is lower than that of visible and infrared spectroscopies; 2) due to the cost and material limitations of UV glass and coating materials, developing optical lenses with large apertures and high transmittance is challenging, this is particularly problematic in telemetry scenarios where UV Raman spectroscopy signals are prone to significant noise; 3) UV fluorescence resonance is stronger than other bands, which can interfere significantly with Raman spectroscopy signals. To address these issues, the characterization capabilities of Raman spectra should be enhanced through noise filtering and baseline spectral processing methods. This study analyzes the principles of solar-blind UV Raman spectroscopy detection in natural environments and reviews global advancements in noise reduction and baseline correction algorithms for solar-blind UV Raman spectroscopy. Furthermore, it examines the potential of this technology in areas such as counter-terrorism, drug control, and food safety. The study also provides an outlook on the development trends of solar-blind UV Raman spectroscopy processing technologies in natural environments.