This study thoroughly examines identifying fraudulent social media users. In social media’s ever-changing climate, identifying dishonest people becomes more important. This study discusses the weaknesses and strengths of current fraud detection methods. Current methods lack precision and accuracy, which can misidentify real users as fake and vice versa. These methods also struggle to handle large datasets, delaying the discovery and removal of fake accounts. To overcome these issues, the authors used Explainable AI (XAI) and Deep Forests to identify fake users. Complex methods improve identification accuracy and reliability while reducing detection times. This study’s theoretical approach thoroughly explains counterfeit user identification and its effects on online social networks. Real-world datasets are used to evaluate the computational model. The suggested method outperforms existing methods by 4.5% in precision, 3.9% in accuracy, and 4.9% in recall. The proposed model reduces latency by 2.5%, a breakthrough. This novel fraud detection method improves online social network security. Thus, it creates a secure, trustworthy virtual environment for users and platform administration.