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The advancement of intelligent, efficient, and comprehensive technologies for testing food quality has long been a focal point and area of intense research in food science. Traditional methods for testing food quality can only assess single-scale attributes and often fail to comprehensively evaluate all quality characteristics of food. This paper focuses on the cross-scale analysis of food quality using cross-modal intelligent sensing detection techniques. It provides a comprehensive overview of the development of these technologies in food science and aims to establish a clear framework for their cross-scale analysis and application in assessing food quality. The paper begins by examining mechanisms of food quality decay, quality detection requirements, and key technological advancements. It analyzes interactions among multi-scale key quality parameters and food quality. Subsequently, it discusses specific needs for food quality detection in various application scenarios, addressing research challenges and advancements in key technologies, particularly focusing on cross-modal sensing mechanisms and strategies for decoupling multiple signals. Finally, the paper explores emerging applications of cross-modal smart sensing technologies, emphasizing system integration, smart device integration, and fusion modeling of sensed signals. The development and application of cross-modal intelligent sensing detection technology not only enhance the accuracy and efficiency of food detection but also enable comprehensive assessment of food quality across multiple scales. This provides crucial technical support for food production, processing, and quality control. Ongoing advancements in self-driven sensing design and optimization of data fusion algorithms are anticipated to further improve detection technology, enhancing the accuracy, reliability, and practicality of food quality assessment. Consequently, these advancements significantly contribute to ensuring the quality and safety of food. [Display omitted] • A framework for the application of cross-modal sensing technologies for cross-scale detection of food quality was given. • Decoupled sensing mechanisms and signal calibration strategies for food quality detection were discussed. • A multimodal data fusion method to enhance the accuracy of food quality prediction was analyzed. • Future directions of cross-modal sensing technology in food science and technology were discussed. [ABSTRACT FROM AUTHOR]