International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Md BushraCh SravaniSk Akbar 8 4 (July - September) 2026 07 July-September enFraud DetectionGmail SecurityEmail FraudSpam DetectionPhishing EmailsCybersecurity. Email fraud has become one of the most common cyber threats causing financial losses and compromising sensitive information Traditional spam filtering techniques often struggle to identify sophisticated phishing and fraudulent emails This project presents an intelligent Gmail fraud detection system that combines machine learning techniques with Redditinspired ranking algorithms to improve the accuracy of email classification The system analyses email content using Natural Language Processing NLP extracts textual and behavioural features such as suspicious links keywords sender information and message patterns and then applies a trained machine learning model to classify emails into Ham Spam or Fraud categories To enhance explainability and ranking effectiveness a Redditbased scoring mechanism is incorporated to assign risk scores according to the presence of suspicious characteristics and Communityinspired relevance metrics The proposed system provides not only fraud predictions but also detailed explanations highlighting the factors that influenced each decision A userfriendly Flask web interface enables realtime email analysis probability visualization and featurebased reasoning Experimental results demonstrate that the integration of machine learning and ranking algorithms significantly improves fraud detection performance helping users identify malicious emails more effectively and strengthening email security against evolving cyber threats Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2026 All Rights Reserved