Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives

Faisal A. Alshuwaier, Fawaz A. Alsulaiman

Currently, with significant developments in technology and social networks, people gain rapid access to news without focusing on its reliability. Consequently, the proportion of fake news has increased. Fake news is a significant problem that hinders societies today, as it negatively impacts many aspects, including politics, the economy, and society. Fake news is widely disseminated via social media through modern digital platforms. In this paper, we focus on conducting a comprehensive review on fake news detection using machine learning and deep learning. Additionally, this review provides a brief survey and evaluation, as well as a discussion of gaps, and explores future perspectives. Through this research, this review addresses various research questions. This review also focuses on the importance of machine learning and deep learning for fake news detection, by providing a comparison and discussion of how they are used to detect fake news. The results of the review, presented between 2018 and 2025, with the most commonly used publishers being IEEE, Intelligent Systems, EMNLP, ACM, Springer, Elsevier, JAIR, and others, can be used to determine the most effective algorithm in terms of performance. Therefore, articles that did not demonstrate the use of algorithms or performance were excluded.