AI-Based Fake News Detection Using Machine Learning: A Review Of Multilingual Detection, Automated Fact-Checking And Messaging-Platform Misinformation

Misinformation spreads not only through websites and social networks but also through messaging platforms and in many languages, which is especially relevant in India. This paper reviews AI-based fake news detection using machine learning with emphasis on three aspects that are less covered by English-only text classifiers: multilingual detection, automated fact-checking and misinformation on messaging platforms. It discusses the social science of fake news and the characteristic features of false articles, such as sensational titles and simpler language, and reviews machine learning classifiers trained on labelled datasets. Automated fact-checking pipelines that identify check-worthy claims, retrieve evidence and verify claims against it are examined, together with cross-lingual transformer models and resources for Indian languages. Studies of misinformation on messaging platforms and pandemic-related fake news datasets are discussed. A five-layer architecture comprising multilingual ingestion, preprocessing, classification, evidence-based verification and user-facing explanation is proposed, along with research gaps, objectives, a comparison of approaches and challenges such as low-resource languages, encrypted messaging, evolving narratives and freedom-of-expression concerns. The review concludes that combining machine learning classification with evidence-based fact-checking provides more trustworthy detection.