Food quality and safety have become critical concerns in recent years, particularly with the increasing incidences of food adulteration. Chromatographic techniques, such as Gas Chromatography (GC), High Performance Liquid Chromatography (HPLC), and Thin-Layer Chromatography (TLC), have been extensively used for the detection of adulterants and contaminants in food products. However, these techniques often require skilled personnel and complex processes that can be time-consuming and expensive. Integrating machine learning (ML) with chromatographic methods can enhance the efficiency, accuracy, and scalability of food safety assessments. This paper explores the role of chromatography in food quality control and how ML can be used to automate, optimize, and improve chromatographic analyses, leading to more effective and reliable food safety monitoring