In response to the growing threat of fake news in our digital era, this paper aims to propose a machine learning system to identify the fake news. Its primary aim is to accurately discern between authentic news articles and deceptive misinformation. The urgency for such a solution arises from the significant consequences of fake news, including the erosion of trust, societal polarization, and potential safety risks. This paper employs machine learning algorithms to analyses textual and contextual elements within news articles. This entails data collection, pre-processing, and model training, followed by a comprehensive evaluation of the model’s performance against established benchmarks. In conclusion, this project is dedicated to fortifying the ongoing struggle against fake news, safeguarding the reliability of information sources, and nurturing a society that thrives on accurate and verified information. The proposed ML system is compared with the baseline methods and results are plotted using various parameters precision, sensitivity, specificity, accuracy and f1score. The proposed model exhibits an accuracy of 96.65% which is promising when compared with existing literature.