This paper uses Deep Neural Networks (DNNs) to identify bogus job ads on recruitment portals. Fake job ads have increased due to online job boards. These ads try to trick people into financial scams, identity theft, and fraud. The suggested solution uses linguistic and behavioral features from corporate profiles, job posts, salary data, and posting trends to identify misleading ads. DNN models trained on large recruitment datasets automatically uncover hidden fraud patterns. This lets it accurately identify fake job listings. Advanced input methods include data standardization, feature embedding, and tokenization improve model performance and reduce false predictions. The results show that the deep neural network surpasses traditional machine learning approaches in accuracy, memory, and object detection. A scalable and reliable solution to improve online employment platforms’ reliability, security, and openness protects job seekers from illegal recruiting and cyberfraud.