The rampant increase in the spread of misinformation around the globe on the social media in the wake of the pandemic & international conflicts has urged the innovation in machine learning paradigms of the fake news detection to tackle the threat. Given that the task belongs to the text classification under the subfield of Natural Language Processing, the benchmark defying performance of the transformers-based models such as BERT inspired to use the pre-trained model for text classification using two approaches, one using the BERT for feature extraction and then classifying the text using standard classifiers, a methodology which has not been widely researched, while other involving fine-tuning the BERT for text classification. The various classifier models achieved remarkable accuracy up to 98%, especially Logistic Regression & Multi-layer Perceptron, which was further topped by the fine-tuned model which achieved an accuracy of 98.8% on the balanced test dataset & outperformed existing BERT-based benchmark FakeBERT on the imbalanced dataset with an accuracy of 99.21% for the task of fake news detection.