Artificial intelligence, particularly machine learning, relies on an increased amount of personal data stored in computers. The collection and use of this data has the potential to compromise user privacy and erode confidence in AI systems. To adhere to the “right to be forgotten” legislation, it is necessary to eliminate personal data from computer systems and machine learning models. Adversarial attacks are feasible because of the retention of past data in machine learning systems. Novel machine unlearning approaches are necessary to facilitate the erasure of data from machine learning models. The absence of standardized frameworks and available resources continues to impede the process of machine unlearning. This article offers a thorough examination of machine unlearning, including its theoretical under pinnings, practical executions, possible uses, and methodology. It also promotes future research and progress by highlighting unexplored opportunities and addressing areas of limited understanding.