Fraudulent websites are considered one of the bigger dangers of the field of cybersecurity since they are able to deceive users into giving sensitive information, including accounts credentials and financial data. The traditional phishing detection methods such as blacklist-based systems or rule-based systems have been proven ineffective against phishing attacks that are very recent or even the ones that are new and are never witnessed before. The case at hand is concerned with introducing a machine-learning-based technique of detecting phishing sites based on the examination of the URL-related features. The proposed system has the following presents; extraction of both lexical and structural features out of the URLs and uses some of the supervised learning algorithms to categorize them as phishing websites or legitimate websites. Assessed using standards-truths in comparison with accuracy, precision, recall, and F1 score. As the results of the experiment demonstrate, the proposition has a good working detection performance at higher accuracy and lower false-positive rates. The findings address the effectiveness of machine learning methods to practically implement an effective Phishing detection system, which is much more effective in securing the web, and the campus network in particular..