In today’s digital era, the proliferation of counterfeit websites poses a significant concern, as they aim to trick users into divulging personal and financial details. This study delves into the efficacy of Machine Learning methodologies, such as Extra-Trees Classifier, Extreme Gradient Boosting (XGBoost), and Decision Tree, in identifying fraudulent websites. We trained and assessed the algorithms by extracting various aspects from the content and metadata of websites that included malware, phishing, defacement, and benign websites using a categorical dataset. The findings reveal that the Extra-Trees Classifier yielded the highest accuracy rate (97%), surpassing both the Decision Tree (96%) and Extreme Gradient Boosting (XGBoost) (96.2%). This study emphasizes how effective learning algorithms are for identifying fraudulent websites. Research in this area is promising in terms of improving defenses against digital threats and strengthening them.