The problem of cyber threats was addressed in this work, with a focus on phishing assaults. These attacks may take many forms, all of which put consumers at danger of revealing personal information. There are issues with latency and accuracy with current security methods. This paper aims to address this by introducing a client-side defence mechanism that uses machine learning methods to recognise fake web pages and protect users against phishing. They provide an add-on for Google Chrome called Phishing Catcher, which uses an algorithm based on machine learning to determine whether a URL is trustworthy or suspicious by analysing site characteristics. The experimental results show that the system can discern between phishing and authentic URLs with an astounding precision and precision of 98.5%.