Authors - Tanay Balakrishna, Vishal Kumar Rahul, Yugabharathi E, Samanvi P, Vinay Joshi Abstract - The rapid spread of online news has made it more difficult to distinguish factually based reporting from misleading content. Many factchecking systems fail to detect false articles that appear professional and realistic, which leads to widespread disinformation. Most models rely on surface characteristics and neglect semantic coherence and factual consistency. An Improved Hybrid Fact-Checking System that combines language understanding, adversarial training, rule-based plausibility checks, and claim level web verification. These components run together in an ensemble model using BERT, BiLSTM, and an XGBoost meta-classifier to merge multiple evidence sources. Experiments on benchmark and curated datasets show an accuracy of 96.84% and a recall of 98%, outperforming existing deep learning methods. The results show that blending linguistic analysis with external verification leads to a robust and interpretable approach for automated fact-checking
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