Wheat flour processing is characterized by bulk handling, continuous operation, and batch-based production. Physical intermingling during batch blending and flour formulation weakens the correspondence between material entities and external batch identifiers, thereby increasing the complexity of quality anomaly localization and recall range determination. To improve the traceability of wheat flour under blending conditions, this review focuses on multimodal information fusion technologies and summarizes quality control requirements throughout raw wheat cleaning and tempering, milling and sifting, batch blending and flour formulation, as well as product packaging and storage. The applications of machine vision, near-infrared spectroscopy, and hyperspectral imaging in surface feature extraction and internal physicochemical component analysis are reviewed, and the limitations of single-modal sensing under dynamic powder-flow conditions are analyzed. Key technologies, including heterogeneous data alignment, feature-level fusion, decision-level fusion, cross-modal fusion, quality fingerprint generation, blockchain-based data recording, and intelligent traceability, are systematically summarized. Existing studies indicate that multimodal fusion can integrate appearance characteristics, internal composition, and processing-state information of wheat flour to form continuous quality representations with batch discrimination capability, thereby providing material-property evidence for quality anomaly identification, candidate-source screening, and recall decision-making. This review provides a reference for multisource sensing configuration, quality fingerprint construction, and intelligent traceability system design in wheat flour processing.