In recent years, as Internet usage has grown, cybersecurity has become a serious concern for computer systems. Malicious websites are a key component of online criminal activity. Various harmful URLs emit various malicious software and attempt to collect user information. The information might be of any type and in any format. There are references to top classification data, business information, financial information, high-brow belongings information, private information, IOT & IIOT, and large-scale information. Identifying and dealing with harmfulwebsites has previously been difficult due to the difficulty in distinguishing between useful and undesirablewebsites. Regardless, suchwebsitesmay nowbe detected using machine learning algorithms on massive datasets. Classifiers built with algorithms, for example, naïve bayes and logistic regression etc., may be utilized to identify malicious websites and caution people before they visit them. Applying the Dangerous and BenignWebsite Dataset, this project focuses on determining if a website is harmful or not using a variety of classification approaches.