The food processing sector has experienced significant impacts from the rapid advancement of technology, both in positive and negative ways. A global challenge we currently face is the adulteration of food products with either safe or harmful substances, which can harm human health or lower food quality. Recent cases have emerged where food adulteration is so subtle that traditional detection methods are ineffective. Hence, there is an urgent need to explore intelligent methods for the precise identification of these adulterants in food items. In this digital age, it is crucial to develop advanced techniques that are quick, user-friendly, cost-effective, environmentally friendly and reliable. To begin with, this paper conducts a comprehensive review of various approaches employed for detecting adulteration in commonly consumed food items such as milk, edible oils, honey and powdered spices. Furthermore, this paper introduces an innovative approach, presenting a detailed procedure for identifying adulteration using a Deep Learning model in combination with Image Processing. This approach has the potential to offer fresh perspectives and guidance for conducting advanced experimental research in this field.