Fake news is an information that has been carefully manipulated to mislead readers by using false facts and figures. Since the introduction of the Internet and social media, fake news has grown to be a significant problem. Identifying fake news has become an important area of research in Natural Language Processing (NLP). The key challenge is determining the veracity of news stories. There is an increasing difficulty in studying and designing a technological strategy to combat fake news without compromising speed and collaborative access to high-quality information. Despite the fact that various technologies have been developed to assist in the detection of false news, and despite significant breakthroughs, identifying fake news stays ineffective. In this research, a new framework has been proposed that utilizes Porter Stemmer, TF-IDF vectorizer for pre-processing and double layer Bi-LSTM for extracting the refined features to obtain better learning. In this model, initially, the summarized input vector is formed by concatenating the most relevant text attributes such as headlines, news for further process. The performance of the proposed model has been justified by evaluating its performance on three experimental datasets namely Kaggle fake_real_news, Liar and Politifact Fake_Real.