The authors offer an operational method for obtaining cyber intimidation intelligence from diverse social platforms on the internet, notably dark-web and deep-web sites with Tor, in this study. They concentrate their efforts on gathering information from hacker forums and marketplaces that sell harmful hacking-related items and services. They've established an operational mechanism for gathering information from these sites. This system now collects 400 high-quality cyber-intimidation notifications every week on average. These danger alerts provide details on newly generated malware and exploits that have yet to be used in a cyber-attack. This is a valuable service for cyber-surveillance. Various machine learning approaches are used to dramatically improve the system. They can recall 93% of items in marketplaces and 85% of comments on forums about harmful hacking with great precision using machine learning models. They do preliminary analysis on the data gathered, illustrating how it might be used to assist a security professional in improved intimidation analysis.