The rapid expansion of social media platforms and online news consumption has led to an increased spread of fake news, posing significant challenges to society by influencing public decision-making and causing severe consequences in domains such as politics and healthcare. Traditional methods for identifying fake news are often too slow to combat its swift dissemination. Therefore, identifying and addressing misinformation is crucial for maintaining the accuracy and trustworthiness of information disseminated on social media. Natural language processing (NLP) and artificial intelligence (AI) play a vital role in this endeavor by facilitating the effective examination of extensive datasets to uncover patterns that are suggestive of misinformation. Machine learning models, particularly transformer-based architectures, enhance fake news detection by improving accuracy and interpretability. The integration of Explainable AI (XAI) methods further enhances transparency and trust in these models.