Hybrid deep learning models can assist in identifying online employment scams. These systems utilize convolutional and recurrent neural networks to identify fraud. We may see how individuals interact with job adverts and with each other across time and across different locations. Datasets characterized by significant noise or irregular spacing can be addressed using sophisticated preprocessing and embedding techniques. We acquired this data by the concurrent analysis of job advertisements, posting activities, business profiles, emails, and chat communications. The amalgamation has facilitated the differentiation between efficacious and inefficacious working approaches. ROC-AUC, F1-score, accuracy, and recall are examples of performance measures. These classifiers surpass the leading machine learning models, as evidenced by testing. When individuals comprehend the mechanics of forecasts, they are more inclined to place their trust in them. To enhance confidence, the technology may autonomously detect and notify users of scams on employment platforms.