Food adulteration is common around the globe. To make sure to get high-quality food, and to identify the numerous adulterants in food items. The use of machine learning and deep learning techniques in the detection of food adulteration has gained increasing attention in recent years. This review paper presents an overview of the current state of the art in detecting food adulteration and summarizes various techniques and applications used to detect food adulteration, including traditional analytical techniques and machine learning. The paper also includes a summary of the recent research papers, which includes the objective, techniques, and samples used for adulteration detection in various food items. Additionally, the paper highlights the challenges faced in detecting food adulteration and the rapid evolution of adulteration methods.

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