Md. Bushra , G Venkata Ravali , Sk. Akbar 3 3 (July - September) With the rapid expansion of social media and other digital platforms the dissemination of false information through these channels has become widespread leading to difficulties in determining the authenticity of online content Moreover factchecking through traditional means is very slow and cannot efficiently manage the enormous volume of information This paper offers a deep learningbased misinformation detection system as a solution to this problem According to the paper the system utilizes various NLP Natural Language Processing techniques such as text cleaning tokenization and feature extraction in order to convert text data into a form that can be easily used for learning by deep learning algorithms A Long ShortTerm Memory LSTM deep learning model is used as the corebased encoding mechanism to capture the underlying semantics and contextual clues hidden in the text that help determine whether the text is genuine or fake The system was subjected to a rigorous evaluation process using wellknown labelled datasets while relying on widelyaccepted performance metrics including accuracy precision recall and F1score Results from the experiments indicate that the system proposed in this paper not only outperforms existing systems in terms of accuracy but also embodies an efficient scalable and trustworthy method for automatically detecting fake news Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026