Fake news, a digital issue, poses a threat to social stability, democracy, and political process decency. This systematic literature review examines the state-of-the-art in fake news detection, focusing on progress achieved by existing methods and challenges researchers and practitioners face. A social media literacy tool was developed to help students differentiate between real and fabricated content. Detection systems often use machine learning and natural language processing approaches, with deep learning models capturing new relationships within text/metadata. Fact-checking methodologies, such as automated and human-assisted methods, validate claims from reliable resources. A hybrid model combining multiple detection techniques increases accuracy and stability. However, the ever-changing nature of fake news, its nuanced nature by context and culture, and the lack of large-scale high-resolution benchmark datasets pose challenges. Research and development directions include cross-platform data integration, explainable AI model development, and real-time techniques for fake news tracking.