N. ANIL KUMAR, M. BHAVYA SREE, M. LAKSHMI GANESH, CH.SYAM VITAL KUMAR, FARHAT SULTANA 03 03 The rapid growth of social media platforms has significantly transformed the way information is produced shared and consumed across the world While these platforms provide a powerful medium for communication and information exchange they also facilitate the rapid spread of fake news and disinformation Such misinformation can influence public opinion disrupt political processes and create social instability Traditional factchecking mechanisms rely heavily on manual verification which is timeconsuming resourceintensive and incapable of handling the massive volume of online content generated daily Consequently automated approaches using Machine Learning ML and Natural Language Processing NLP have emerged as promising solutions for identifying and mitigating fake news This study presents a systematic review and comparative analysis of machine learning and deep learning techniques used for fake news detection In particular the research focuses on the performance comparison between Random Forest a widely used machine learning algorithm and Long ShortTerm Memory LSTM a deep learning architecture designed for sequential text processing Random Forest utilizes textual features extracted through TFIDF vectorization while LSTM leverages word embeddings to capture contextual semantics and longterm dependencies within textual data The proposed framework includes data collection from benchmark datasets text preprocessing feature extraction model training and evaluation using performance metrics such as accuracy precision recall and F1score The results demonstrate that deep learning models capture contextual patterns more effectively whereas traditional machine learning models provide faster training and interpretability The study highlights the importance of combining linguistic features with deep contextual learning to improve misinformation detection Furthermore it identifies research gaps and future opportunities including multilingual detection systems transformerbased models and realtime deployment for social media monitoring Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026