As stuck pipe holds on to be a prime contributor to non-productive time (NPT) in drilling industry operations, efforts to restrict its incidence cannot be over-emphasized. Historically, stuck-pipe events have been shown to cost the industry several hundred million dollars annually and over 25% of non-productive time. British Petroleum Company's stuck pipe costs exceed $30 million annually. Industry-stuck pipe costs are estimated to be above $250 million annually. Major causes of this issue involve wellbore instability, differential sticking forces, improper hole cleaning, and the forming of drill-cutting beds, especially in high-angle wells. One strategy for avoiding stuck pipe issues is to predict by using the available drilling data, which can be utilized to adjust drilling parameters. Preventing stuck pipes requires close monitoring of early warning signs, such as increases in torque and drag, excessive cuttings loading, tight spots while tripping, and loss of circulation while drilling. A machine learning (ML) approach was employed to identify warning signals and anticipate stuck pipe events due to its ability to handle complex parameter relationships. This article proposes an extensive comprehensive review of challenges associated with pipe-sticking issues to detect warning signs and early indicators of a stuck pipe during drilling to prevent it and provide operational recommendations for avoiding or freeing stuck pipes. Finally, this research paper analyzes and consolidates the idea of the importance of Artificial Intelligence (AI) methods for predicting the condition of stuck pipes during well drilling.