Fake news is termed as the news that spreads via the internet very fast which is not true i.e., false news. Since we are in a society of modern living culture, we will be attracted to the trend easily. So, taking this as an advantage some business traders make this news as their profit by clicking on that fake news. We can observe these types of issues in areas like Political issues, medical issues, Job rackets, etc. The tremendous increase in the spreading of fake news may result in less hope for real news. The main goal is to create a resilient and effective system with the ability to automatically differentiate between authentic and falsified news articles. We can find whether the news is fake or real through machine learning algorithms with greater methodology. We have selected and implemented a few datasets using machine learning algorithms like Decision Tree, Naive Bayes, SVM, Random Forest, Logistic Regression, and Passive aggressive classifier. Further, we come up with the algorithm which gives the highest performance measures. The results from the experiments exhibit encouraging performance metrics in identifying fake news, highlighting machine learning’s potential in countering misinformation. The outcomes imply that blending various feature types and advanced algorithms leads to better performance when contrasted with individual methods. We have applied these ML methods on two datasets and achieved accuracy of 99.69% with SVM, 99.06% with Logistic Regression and 99.64