Scam websites or URLs put cybersecurity at risk. The cost of malicious URLs, which spread unwanted information (such as junk mail, fraudulent emails, and direct downloads) and trick people into scams (including financial loss, identity theft, and malware installation), is estimated to be billions of dollars a year. Many examples of malicious URLs have been compiled in this dataset so that a machine-learning-based model can be developed to identify risky URLs and prevent them from spreading on the Internet or damaging computer systems. A machine learning model for detection and classification has been created. We employed a decision tree for classification, achieving a testing accuracy of almost 91%, and logistic regression for detection, showing a testing accuracy of nearly 99.6%. On top of that, we developed and set up a web extension.