The swift rise of virtual media has caused more growth in the distribution of false information, or “fake news.” With this rise, fake news has the ability to sway public sentiments, instigate panic among individuals, and even affect the ability of people to make decisions. Finding these types of content, especially on a large scale and automatically, is one of the main AI/NLP challenges today. The purpose of this research is to build a lightweight and explainable AI system that can identify fake news by reviewing numerous analytical aspects that contribute to its credibility, including; Natural Language Processing (NLP) analysis, rule-based techniques, checking the credibility of the source of the news, identifying clickbait, verifying the reputation of the domain. The system provides a “credibility score” based on a scale of 0-100 and classifies articles into Likely Credible and Fake News. It can accept an input of either text, PDF, or an image through OCR and produces a result very quickly. It also maintains transparency and user privacy