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With The Rapid Growth Of Digital Media And Online Platforms, The Spread Of Fake News Has Become A Critical Challenge, Influencing Public Opinion, Creating Social Unrest, And Threatening The Credibility Of Information Sources. Traditional Manual Fact-checking Methods Are Time-consuming And Insufficient To Handle The Massive Volume Of Online Content. To Address This Issue, This Work Proposes A Machine Learning–based Framework For Automatic Fake News Detection. The System Leverages Natural Language Processing (NLP) Techniques To Extract Meaningful Features Such As Linguistic Patterns, Semantic Relationships, And Contextual Cues From News Articles. These Features Are Then Classified Using Supervised Learning Algorithms, Including Logistic Regression, Support Vector Machines, And Random Forest, To Distinguish Between Fake And Legitimate News. The Proposed Framework Is Evaluated On Benchmark Datasets, Demonstrating Improved Accuracy And Robustness Compared To Baseline Models. This Study Highlights The Potential Of Machine Learning In Combating Misinformation And Provides A Scalable Solution For Enhancing The Reliability Of Digital News Consumption.