This project explores the application of machine learning models and advanced natural language processing (NLP) techniques to detect and mitigate misinformation, commonly referred to as “fake news.“ As misinformation becomes increasingly prevalent, public trust in media declines, leading to significant societal and economic repercussions beyond mere deception. The spread of false information impairs individuals’ ability to make well-informed decisions, ultimately distorting public discourse and shaping opinions based on inaccurate narratives. This study aims to examine the role of machine learning in combating misinformation and to develop a tool that enables individuals to verify the credibility of news content, thereby addressing the growing distrust between the public and the media. Leveraging Amazon Web Services (AWS), including Simple Storage Service (S3) for data management and SageMaker for model training and inference, this research establishes a robust foundation for misinformation detection. Key methodologies include the implementation of Long Short-Term Memory (LSTM) networks and the XGBoost algorithm to enhance the accuracy of fake news classification. By tackling the challenges associated with misinformation, this project aspires to contribute to a more informed society. However, ongoing research remains essential to further refine detection methods and safeguard media consumers against deceptive content.