Fake news is a big problem today, especially on social media, where misinformation spreads quickly. To solve this, researchers use artificial intelligence (AI) and machine learning (ML) to detect fake news. This study explores different AI models, including BERT, ALBERT, and ROBERT, which help analyze text and classify news as real or fake. Advanced deep learning methods work better than traditional machine learning because they understand language more deeply. Some methods also check reliable sources, analyze emotions in text, and use social network data to improve accuracy. However, there are still challenges, like biased training data and new ways of spreading misinformation. This paper reviews existing fake news detection techniques, compares their performance, and suggests a strong AIbased solution to detect fake news more effectively. The goal is to make online information more trustworthy and limit the spread of misleading content.
Fake News Detection, Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning Algorithms, Text Classification, News Credibility Analysis, Deep Learning Techniques, Semantic Analysis, Misinformation Identification, Real-Time Content Verification.