As a core sector of the food industry， grain and oil processing urgently requires efficient and precise quality detection technologies to drive its intelligent transformation. Visible/Near-infrared (Vis/NIR) spectroscopy， with its advantages of rapid， non-destructive， and multi-parameter synchronous detection， has emerged as a pivotal tool for quality monitoring in grain and oil processing. This paper systematically reviewed the latest advancements in NIR detection principles， intelligent equipment development， and spectral data processing methodologies. At the hardware level， breakthroughs in miniaturization and anti-interference technologies had enabled portable and online monitoring devices to transition from laboratory research to industrial applications. Algorithmically， the integration of spectral preprocessing， variable selection， and intelligent modeling had significantly enhanced detection accuracy and robustness. In practical applications， the technology had been deployed across critical stages such as moisture regulation in grain processing chains and oxidation monitoring during oil refining， driving a shift toward data-driven quality control. However， challenges persist， including limited model generalization， weak adaptability to complex industrial environments， and the absence of standardized systems. Future advancements demanded collaborative innovation in 'algorithm-equipment-standard' systems through deep transfer learning， multi-source information fusion， and edge computing technologies to achieve real-time quality regulation and intelligent upgrades across the entire grain and oil processing chain.