The rise of social media and online news platforms has resulted in a concerning surge in the dissemination of false information. This trend poses a significant danger to open dialogue, democratic processes, and societal cohesion. Utilizing machine learning and deep learning methodologies has shown promise in the fight against fake news. This article presents a thorough examination of the current advancements in identifying fake news through deep learning and other machine learning techniques. Various methods of extracting features and classifying information in this field are discussed, along with an evaluation of different models’ performance on established datasets. Furthermore, this paper identifies the obstacles and constraints of present strategies and delves into potential avenues for future research.