The rapid expansion of digital media has accelerated information dissemination, but it has also intensified the spread of misinformation that can negatively impact social and political stability. This challenge has driven the development of intelligent algorithms capable of detecting unreliable content through advanced text analysis. Designing such algorithms involves multiple stages, starting with data collection from diverse sources, followed by text cleaning and preprocessing using noise reduction and feature extraction techniques. The textual data is then transformed into numerical representations using word embeddings or advanced transformer-based models. Deep learning architectures are trained to capture semantic context and relationships between words, enabling the identification of patterns typical of fake news, such as emotional exaggeration or lack of credible references. Additionally, integrating social network analysis can help detect abnormal dissemination patterns. These systems support media organizations and social platforms in limiting misinformation spread and promoting digital credibility. However, algorithm design must ensure fairness, transparency, and respect for freedom of expression. Consequently, fake news detection represents both a technical and ethical challenge that requires interdisciplinary collaboration among AI specialists, media experts, and legal scholars.