In recent years, cybercrimes have been increasing tremendously and affecting victims around the world.Cybercrime analysis and detection help protect and prevent targeted unwary users and turning them into victims. Scams (money loss, identity theft, and malware installation) re- sult in losses of billions of dollars each year. It is crucial to quickly identify and respond to such dangers. Blacklists are used most often to detect criminals in general. However, blacklists are not thorough and do not include the ability to recognize freshly created malicious Websites.The generality of malicious URL detectors has to be improved, It has become necessary to build a reliable system to detect malicious websites by analyzing related data. Although, there are sev- eral cybercrime detection techniques such as Statistical Methods and Machine Learning. Thus, our project sets out to build a machine learning model and develop a website to detect malicious URLs. The used dataset had 651,191 URLs containing safe URLs, defacement, phishing, and malware URLs. We built a model using the Random Forest algorithm and were trained with our dataset. The model had 96.85% Accuracy. Therefore, we integrated the model with the website we designed which had a simple UI/UX.