Identifying human players and bots in online gaming is challenging, especially when player behavior deviates from established norms. Bots exploit game mechanics to gain unfair advantages, leading to cheating, leaderboard distortions, and a diminished player experience. Advanced bots can mimic human actions, making detection difficult for traditional anti-cheat systems. To address these issues, this study proposes a hybrid approach combining clustering techniques and neural net- works for improved player behavior analysis. By leveraging ensemble learning with machine learning algorithms and artificial neural networks, the method achieves up to 98% accuracy in classifying players in the MMORPG AION. Real-time monitoring, adaptive models, and community-driven reporting further enhance detection capabilities. Given that bots often exploit game economies for financial gain, precise identification is essential to maintaining competitive fairness. The proposed approach not only strengthens player classification but also deepens the understanding of complex behavior patterns, contributing to more effective bot mitigation in online gaming. This research underscores the importance of integrating advanced analytics to preserve balance and integrity in gaming environments, ensuring an engaging and fair experience for all players.