One of the most popular and widely utilised platforms for digital marketing, social media allows firms to keep tabs on public trends and preferences, and it also provides valuable insights into consumer behaviour. The number of false social media accounts that disseminate misinformation is on the rise. In order to address the issues surrounding the identification of false social media profiles, this study examines several machine learning techniques.Jupyter Notebook makes use of Python and a number of machine learning and data analytics libraries, including Numpy, Pandas, Sklearn, and others. Using AUC Score, Confusion Matrix, and total number of Fake and Genuine Users discovered, this article compares three machine learning algorithms: Support Vector Machines (SVM), Random Forest, and Neural Networks. For easier examination and comparison across all methods, results are shown as graphs. The dataset that was used for this project can be found in the following link: At t