Saffron, also known as the “golden spice”, is one of the costly spices cultivated mainly in Iran, Greece, Spain, China, and India (mostly in Kashmir valley). However, due to its limited production and high price, it is often subjected to the issue of adulteration which leads to economic loss and several health issues. The various types of adulterants that are mixed with original saffron samples are sunflower, safflower, maize outer covering, and hibiscus stigmas to increase their quantity. Nowadays, the menace of saffron adulteration is one of the major issues that need further attention as human vision fails to discriminate between pure and adulterated saffron samples. Hence, an intelligent computer vision-enabled saffron adulteration prediction system is used by researchers to overcome this issue. In this paper, we present a novel framework for saffron adulteration prediction using intelligent data-driven approaches. Moreover, various pre-processing techniques enhance the quality of saffron images. Besides, we demonstrate the comparative analysis of recent saffron adulteration prediction techniques and identify certain open research issues that need futuristic attention. In addition, performance protocols such as performance metrics and saffron images-based data are briefly illustrated. Our analysis reveals that this study provides researchers with new directions that need to be addressed in the future and can potentially prevent economic loss and ensure consumer safety.