Fake news poses a significant danger to the accuracy and reliability of online content. This research study investigates the use of Machine Learning (ML) techniques, such as Logistic regression, Support Vector Machines (SVM), Random Forest, and Artificial Neural Network (ANN), to verify articles. It examines existing literature to emphasize the difficulties in distinguishing genuine from counterfeit digital content. The research aims to provide a complete framework for identifying and alerting to fake news and promoting the dissemination of correct and reliable information.  Support Vector Machines (SVM), Logistic Regression, Random Forest, and Artificial Neural Networks (ANN) algorithms provide a practical and easily understandable solution for individuals and organizations seeking to combat the spread of misinformation online. Among them, the Logistic Regression model outperforms the SVM and Random Forest classifiers in identifying fake news. This research contributes to enhancing the quality of online material and facilitating comprehension. The discoveries enhance software quality and user satisfaction by actively managing significant issues.Fake news poses a significant danger to the accuracy and reliability of online content. This research study investigates the use of Machine Learning (ML) techniques, such as Logistic regression, Support Vector Machines (SVM), Random Forest, and Artificial Neural Network (ANN), to verify articles. It examines existing literature to emphasize the difficulties in distinguishing genuine from counterfeit digital content. The research aims to provide a complete framework for identifying and alerting to fake news and promoting the dissemination of correct and reliable information.  Support Vector Machines (SVM), Logistic Regression, Random Forest, and Artificial Neural Networks (ANN) algorithms provide a practical and easily understandable solution for individuals and organizations seeking to combat the spread of misinformation online. Among them, the Logistic Regression model outperforms the SVM and Random Forest classifiers in identifying fake news. This research contributes to enhancing the quality of online material and facilitating comprehension. The discoveries enhance software quality and user satisfaction by actively managing significant issues.