<p>Traditional analytical techniques for food analysis, such as HPLC and GC–MS, face limitations including high cost, operational complexity, and a lack of portability. Electrochemical sensors offer a promising alternative, providing rapid, sensitive, and on-site detection of various food contaminants like heavy metals, pesticides, allergens, and pathogens. However, the complexity of food matrices and the resulting sensor data often require advanced interpretation. This is where chemometricals, employing statistical and machine learning algorithms such as PCA, PLSR, and SVM, become crucial for extracting meaningful patterns, classification, and predictive modeling from complex electrochemical signals. This review systematically explores the synergistic combination of electrochemical sensing and chemometrical analysis, highlighting its successful applications in food authenticity, adulteration detection, freshness monitoring, and quality control across diverse products like dairy, coffee, and juices. While challenges such as matrix effects, electrode stability, and model validation persist, the integrated approach demonstrates significant potential to enhance efficiency, safety, and compliance in the food industry. The novelty of this article lies in its comprehensive and critical synthesis of the chemometrical–electrochemical combinatorial approach, specifically addressing advancements, limitations, and future perspectives to advance intelligent food analysis.</p>