Abstract: Preserved eggs and salted eggs are among the most representative traditional Chinese egg products and are widely favored by consumers because of their distinctive flavor, texture, and nutritional characteristics. During industrial production, however, differences in raw materials, pickling environments, processing parameters, and storage conditions frequently lead to unstable product quality and considerable individual variation. Common quality problems include shell cracking, internal liquefaction, insufficient yolk oil exudation, uneven gel formation, and microbial deterioration. Traditional quality evaluation methods mainly rely on sensory assessment, candling inspection, knocking sound analysis, and physicochemical measurements. These methods are often characterized by strong subjectivity, low detection efficiency, destructive sampling, and poor repeatability, which limits their application in large-scale standardized production and intelligent manufacturing systems. Consequently, the establishment of rapid, objective, and reliable quality evaluation methods has become an important requirement for the modernization of the traditional egg product industry. This review systematically summarizes the research progress of non-destructive testing technologies and related detection equipment applied to traditional egg products. The cultural significance and economic value of preserved eggs and salted eggs are first introduced, followed by an overview of the pickling processes and major quality evaluation indices. Particular attention is paid to the physicochemical transformations occurring during pickling, including salt diffusion, alkaline penetration, protein denaturation, gel network formation, yolk oil release, moisture migration, and internal structural reconstruction. These complex changes directly influence the sensory quality, nutritional properties, and storage stability of traditional egg products, while also increasing the difficulty of feature extraction and quality identification during non-destructive detection. Traditional detection methods and their technical limitations are analyzed in detail. On this basis, recent advances in machine vision, spectral analysis, acoustic and mechanical detection, and emerging intelligent sensing technologies are comprehensively reviewed. Machine vision technology has been widely employed for external quality inspection, including crack detection, shell defect recognition, texture analysis, and color feature extraction. Combined with image processing algorithms and deep learning approaches, machine vision enables automated and high-throughput quality evaluation. Spectroscopic techniques, such as near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, and ultraviolet-visible spectroscopy, have demonstrated strong capability for internal quality analysis by identifying characteristic information related to protein degradation, lipid migration, moisture distribution, and chemical composition changes. Acoustic and mechanical detection methods provide additional approaches for evaluating shell integrity, internal viscosity, and structural stability through vibration signals, impact responses, and elastic characteristics. Furthermore, advanced technologies including X-ray imaging, terahertz sensing, electronic nose systems, and multimodal information fusion methods have gradually been introduced into traditional egg product detection, improving detection accuracy, automation level, and adaptability under complex industrial conditions. The advantages, limitations, and practical application challenges of current non-destructive testing technologies are further discussed. Existing studies still face several bottlenecks, including insufficient robustness of detection models, unstable signal acquisition under highly variable pickling conditions, limited online detection capability, and the lack of unified quality grading standards. Future development should focus on integrating artificial intelligence, intelligent sensing, digital image processing, and multimodal data fusion technologies to achieve more accurate, adaptive, and real-time quality assessment. In addition, establishing full-process digital traceability systems and multidimensional standardized grading frameworks will be essential for promoting intelligent quality control and industrial upgrading. The development of non-destructive testing technologies is expected to accelerate the transformation of traditional egg product inspection from empirical judgment to data-driven intelligent analysis, thereby improving food safety, production efficiency, and product consistency, while supporting the sustainable and high-quality development of the traditional egg product industry.