This paper aims to address the persistent threat of phishing attacks by developing PhishCatcher, a client-side defense tool. The primary goal is to utilize machine learning as a core component for the robust identification of evolving web spoofing threats. Recognizing the escalating risk posed by phishing, especially in the context of increased online activities, this paper signifies the urgency of countering web spoofing. PhishCatcher is designed with the end-user in mind, especially those who are frequently targeted by phishing attacks. We extended our anti-phishing tool by integrating Support Vector Machine, XGBoost, and a Stacking Classifier, augmenting the system’s capabilities. Additionally, a Flask framework with SQLite was implemented, offering streamlined signup and sign-in processes for user testing and input validation.