Cybercrimes are consistently advancing in complexity. Cyber threat intelligence (CTI) is useful for analyzing, evaluating, and drawing conclusions for areas such as indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs). However, valuable CTI is not always utilized effectively, particularly from the unregulated Dark Web. Manually processing this vast and complex data is unrealistic. This research uses artificial intelligence (AI) and machine learning (ML) to provide automation and process a sizable amount of information in a reasonable time. Specifically, it will use the DUTA-10K Darknet dataset to develop ML models based on the Random Forest, Support Vector Machine, and XGBoost algorithms. These models distinguish illicit and benign addresses, along with their classes and language categories. The resulting models showed reliable performance, with XGBoost consistently producing more effective models overall for predicting illicit status and language.