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Phishing websites are one of the most common cyber threats, designed to trick users into revealing sensitive information such as usernames, passwords, and banking details. Traditional phishing detection methods like blacklists and rule-based systems are often ineffective against newly created and sophisticated phishing websites. To overcome these limitations, this project proposes a machine learning-based phishing website detection system that automatically classifies websites as legitimate or phishing. The system extracts important URL-based and domain-based features such as URL length, presence of special characters, SSL certificate status, and domain age. Machine learning algorithms including Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine are trained and evaluated using real-world datasets. The trained model is integrated with a browser-based interface to provide real-time detection and user alerts. Experimental results show that the proposed system achieves high accuracy and effectively detects phishing websites, thereby enhancing user safety and reducing the risk of online fraud.