International Journal of Scientific Research and Engineering Development

The proliferation of digital communication has introduced a significant security and efficiency challenge in the form of pervasive spam. Traditional filtering methods—relying heavily on static rules, manual blacklisting, and basic keyword matching—are often timeconsuming and struggle to keep pace with evolving obfuscation techniques. Given the exponential increase in digital data, an automated and intelligent solution is essential to enhance the speed and accuracy of threat detection. This research proposes an AI-Based Spam Detector that utilizes deep learning and Natural Language Processing (NLP) to automatically identify and filter malicious content from diverse message streams. The system integrates automated text preprocessing with feature extraction through deep neural network models. These extracted linguistic embeddings are analyzed against a comprehensive database of communication patterns using similarity matching and classification algorithms to determine the probability of spam. Experimental evaluation demonstrates that the proposed system provides reliable real-time performance and superior detection efficiency compared to legacy filters. By minimizing manual oversight and enabling automated security alerts, the system supports users and organizations in streamlining communication management. With further optimization and large-scale deployment, this AI-driven solution has the potential to significantly improve digital security and the overall integrity of global messaging ecosystems.

Rizwan Nadaf, Shri.R.Jadhav, Viraj.S.Patil, Pranit .M. Patil, Mrs.Vrushali Bhivase,"AI Based Spam Detector" International Journal of Scientific Research and Engineering Development, V9(2): Page(2034-2039) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.