The Fake news is becoming more and more common due to the significant changes made to the information distribution landscape by the rapid advancement of technology. Misinformation can spread quickly in this environment because digital platforms are widely used and because creating and sharing content is simple. Modern technological solutions are required to identify and address the increasing issue of misinformation in digital media. This study examines cutting-edge methods for detecting fake news’s. This study also presents Machine Learning models that can reliably identify fake news in various challenging scenarios. The machine learning models that are employed are Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). LR, DTC, GBC, and RFC models’ accuracy were approximately 98.69%, 99.69%, 99.56%, and 99.03%, respectively. We also created a Graphical User Interface (GUI). That is built up with a Python library, namely tkinter. We get output on the dialogue box if we input the top box. Attributes are taken according to our choice. As our code is in Python, we created a GUI with Python library rather than preferring HTML, CSS, or other languages.