This paper examines the significant threats posed by fake news in the era of information overload, highlighting its potential to manipulate or suppress individuals, communities, and governments. It offers a widespread analysis of recent advancements for the identification of false information, presenting both practical and theoretical perspectives. Recent breakthroughs include the development of advanced methods of Natural Language Processing (NLP), such as models based on transformer, which enhance the knowledge as well as analysis of text content. Techniques of machine learning, particularly the methods of deep learning, are useful in the accurate identification of fake news by identifying subtle patterns and anomalies in data. Additionally, network analysis has advanced with techniques to map and analyze the distribution of information across social media platforms, helping to determine and track the propagation of fake news. From a practical standpoint, these breakthroughs have led to the implementation of sophisticated algorithms and frameworks. Theoretically, the paper discusses the principles underpinning these technologies and their potential to improve the correctness and effectiveness of the identification of fake information. The study focuses on three main methodologies: NLP for text analysis, social network analysis for examining information dissemination, and source credibility assessment for evaluating the reliability of information sources. These combined approaches aim to provide robust solutions to alleviate the influence of fabricated information.