Phishing is the most straightforward method of obtaining sensitive information from unsuspecting consumers. Phishers want to get their hands on sensitive data such as bank account numbers, usernames, and passwords. People working in cybersecurity are now searching for reliable and effective methods for detecting phishing websites. The goal of this research is to apply machine learning to distinguish between legitimate and phishing URLs by extracting and analysing numerous components. Attackers deceive Internet users by impersonating a legitimate website in order to obtain personal information. Sending emails to respectable corporations or enterprises can also be used. The accuracy of the prediction can be improved using machine learning methods. Phishing site URLs have certain characteristics, in the proposed work, these characteristics are investigated for the detection of phishing websites. The study examines machine learning methods and how well they function to identify phishing websites. Experiments conducted by using a hybrid strategy demonstrate improved accuracy for phishing website detection.