ABSTRACT The Right to Erasure has facilitated the erasure of data as part of ethical and legal compliance for all users. In this regard, Machine Unlearning (MUL) is an emerging tool that transforms the existing trained model to execute comprehensive data erasure. Although beneficial to a few, the rapid development of unlearning algorithms has hindered a beginner's ability to recognize the relationship between the algorithm's productivity and model effectiveness. Though machine unlearning can be quite instrumental in building ethical and trustworthy AI, it is not beyond corrections. Substantive gaps in the form of standardization, formal guarantees, and verifiable implementation do exist. The aim of this paper, therefore, is to present a comprehensive understanding of the machine unlearning field literature and practices. Over 100 peer‐reviewed articles were reviewed, available from 2019 to 2025, including those on federated learning, continual learning, graphical neural networks, and rapidly growing models like generative and large language models. The paper presents a critical analysis of evaluation metrics, unlearning verification, efficiency, model utility on retained data, scalability, and handling of interdependent and multimodal data. Furthermore, it proposes future research directions, pressing the need for effective and efficient unlearning mechanisms. This article is categorized under: Technologies > Machine Learning
Sehgal et al. (Fri,) studied this question.