Phishing presents a widespread and perilous menace within the domain of cybercrime, functioning as a rapid and uncomplicated means for cybercriminals to get confidential personal data. Phishing attacks specifically target unsuspecting persons with the intention of obtaining sensitive information, such as usernames, passwords, and financial credentials. Phishing websites frequently incorporate indicators inside their textual content and browser cookies. In order to address this issue, the suggested method makes use of the UCI Phishing Websites Data and implements categorization based on Extreme Learning Machine (ELM). In contrast to gradient-based algorithms, the ELM approach employs a solitary hidden layer of neurons that include input weights produced randomly. This design facilitates effective feature learning from data with a high number of dimensions, eliminating the need for iterative changes. The evaluation yielded a remarkable accuracy of 95.34% when compared to other machine learning techniques such as Random Forest Classification and Logistic Regression.