<p>Fruit consumption has become a necessity nowadays because maintaining a healthy lifestyle and a nutritionally balanced diet is crucial, given the high-stress lifestyle adopted universally. Fruits are rich in various nutrients, containing multiple vitamins, minerals, and dietary fiber. Thus, fruits have a high demand in markets, and to fulfill that demand, some sellers tend to use artificial ripening agents to ripen climacteric fruits, which have various harmful effects on the human body. Artificially ripened fruits are a primary concern for the Indian fruit industry and a major hindrance to its growth globally and for consumers. This paper explores various non-destructive artificial intelligence (AI) techniques to classify artificially ripened fruits, enabling us to maintain fruit quality and consumer health. These AI techniques are simple, accurate, cost-effective, and scalable compared with the traditionally used methods. This paper will work as a database to help future researchers explore all AI techniques in one place. The analysis suggests using spectral data, specifically hyperspectral imaging with machine learning models, for detecting artificially ripened fruits, which needs further exploration. This approach would be advantageous for accessing complex properties, such as surface and internal changes, and would improve detection accuracy.</p>