The spread of fake news has become a serious threat to the population, social cohesiveness, and decision-making due to the rapid growth of online news sources and social media. Alternative fake news is usually crafted in such a manner that it seems credible and manual detection becomes less and less reliable. This paper is a fake news detector framework based on deep learning and developed from a Long Short-Term Memory (LSTM) network to distinguish between a fake and a real news article. The presented system takes advantage of the methods of natural language processing to extract contextual, semantic, and sequential patterns in a textual data set. Thereafter, following extensive preprocessing, such as tokenization, normalization, and stop-word elimination, textual inputs are converted into dense vectors through an embedding layer. The LSTM model is a good model of long-range dependencies in news data, which allows to distinguish between misleading and verbal information better. The experimental findings prove that the proposed model has a high level of performance in terms of accuracy, precision, recall, and F1-score, which justifies its applicability to fake news detection. The results indicate that the LSTM-based models could be implemented as a scalable and trustworthy option in fighting the problem of misinformation within digital media ecosystem.