One of the easiest ways to obtain personal information from careless individuals is Phishing,     Machine     Learning,     URL Detection, Cyber security. through phishing attacks. The main   goal is to acquire phisher's important information, such as bank account details, usernames, passwords, and more. Cyber security experts are currently focusing on creating reliable and powerful identification methods for detecting phishing websites. By extracting and analyzing  several  attributes  from  both legitimate and phishing URLs, this study examines the use of a machine learning approach for phishing URL identification. Phishing  websites  are  classified  using methods such as Support Vector Machines (SVMs), Random Forests, and Decision Tree Algorithms.This study focuses on the use of One of the easiest ways to obtain personal information from careless individuals is Phishing,Machine Learning, URL Detection, Cyber security. through phishing attacks. The main   goal is to acquire phisher's important information, such as bank account details, usernames, passwords, and more. Cyber security experts are currently focusing on creating reliable and powerful identification methods for detecting phishing websites. By extracting and analyzing  several  attributes  from  both legitimate and phishing URLs, this study examines the use of a machine learning approach for phishing URL identification. Phishing  websites  are  classified  using methods such as Support Vector Machines (SVMs), Random Forests, and Decision Tree Algorithms.This study focuses on the use of machine learning  approaches  for  phishing  URL detection  by  extracting  and  analyzing various attributes from both real and phishing URLs. Phishing websites are categorized using Support Vector Machines (SVMs), Random Forests, and Decision Tree Algorithms. In addition to successfully  identifying  phishing  URLs, machine learning  approaches  for  phishing  URL detection  by  extracting  and  analyzing various attributes from both real and phishing URLs. Phishing websites are categorized using Support Vector Machines (SVMs), Random Forests, and Decision Tree Algorithms. In addition to successfully  identifying  phishing  URLs