The dark web dating back from the 1970s encompasses an unknown part of the internet that has an abstract and protected layer. Its uses range from the illegal breaching of internet security and drug trafficking to ransomware attacks. Tracking threat actor’s activities is impossible because of the dark webs’ decentralized infrastructure, with anonymity networks such as TOR and I2P. To counter this issue, deep learning methods like convolutional neural networks (CNNs), recurrent neural networks (RNNs) also called Long Short-Term Memory (LSTM) networks, and transformers (e.g. BERT) have been proposed in this research with the aim of developing a dark web security enhancement system. These models deal with challenges of encryption, anonymity, multilingualism, actively changing threats, and among others as encryption-deficient algorithms with content categorization, harmful activity detection, and anomalous behavior forecasting. The system has shown to reach 93.13% accuracy which proves that the proposed model is beneficial for proactive engagement in cybersecurity systems as real-time threat detection is achieved with minimal false alarms. Lastly, the paper contributes to the discussion of ethical challenges regarding privacy and artificial intelligence misuse by outlining reasonable measures to combat cybercrime and protect user rights.