The growing food industry has led to the continuous expansion of the market of soy sauce and pot-roast meat products. However, traditional quality control methods have inherent limitations such as strong subjectivity, low efficiency, and poor predictability in areas including raw material selection, processing techniques, and flavor analysis, which severely restrict the high-quality development of the soy sauce and pot-roasted meat products industry. Machine learning, as an advanced data analysis and modeling technique, offers new solutions to these challenges. Against this background, this review discusses the application of machine learning in the quality control of soy sauce and pot-roast meat products, focusing on the assessment of raw meat freshness, analysis of processing suitability, selection and blending of spices, optimization of processing techniques, standardization of flavor prediction, quality grading based on data fusion, and shelf-life prediction. It also explores the current challenges and future trends in order to provide a technical reference for the quality control of soy sauce and pot-roast meat products.