ABSTRACT The rapid growth of social media platforms has significantly increased the spread of misinformation and fake news across digital networks. Fake news can influence public opinion, affect political processes, and create social instability. Detecting false information automatically has therefore become an important challenge in the field of data science and natural language processing. Traditional manual fact-checking methods are time-consuming and inefficient for handling the large volume of online information generated daily. Machine learning techniques provide efficient tools for identifying patterns in textual data and distinguishing between legitimate and misleading information. This study proposes a machine learning-based approach for fake news detection using natural language processing techniques. Textual features are extracted using Term Frequency–Inverse Document Frequency (TF-IDF) representation, and classification algorithms such as Logistic Regression, Naïve Bayes, and Random Forest are applied for prediction. The performance of the proposed models is evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results demonstrate that machine learning models can effectively identify fake news articles and provide reliable performance for automated misinformation detection systems. Key words: Fake News Detection, Machine Learning, Natural Language Processing, Text Classification, Social Media Analytics