The constant evolution of adversarial techniques in cybersecurity presents a significant challenge to conventional defense mechanisms. This study explores adversarial machine learning (AML) to enhance cybersecurity systems by introducing a new hybrid method that combines generative adversarial networks (GANs) and random forests. The proposed model demonstrates an impressive accuracy of 98.89%, highlighting its effectiveness in enhancing cybersecurity defenses. The increasing complexity of cyber threats requires creative solutions in machine learning for detecting and preventing intrusions. Adversarial machine learning is a promising field that emphasizes the resilience of models against adversarial attacks. This study focuses on the critical necessity for improved cybersecurity by combining generative adversarial networks (GANs) and random forests to leverage the advantages of both in a cooperative way. The suggested method starts by using GANs to create artificial data, which helps enhance the training dataset. These artificial data, which closely mimics real-world examples, offer a varied and thorough learning setting for training models. The random forests algorithm is used to uncover the inherent patterns in the data due to its robustness and interpretability. The model is trained using the BOT-IOT dataset, which is a widely recognized dataset in the field of cybersecurity. The hybrid GAN-random forest model demonstrates a remarkable accuracy of 98.89%, indicating its ability to distinguish between normal and adversarial instances with great precision. The accuracy of the hybrid model is confirmed through thorough experimentation, covering different adversarial scenarios and real-world cyber threats. Adversarial training techniques are used repeatedly to improve the model's ability to resist manipulation and remain adaptable to changing threat environments. The research introduces a new approach in Adversarial Machine Learning for Cybersecurity by combining the capabilities of GANs and Random Forests. The model's accuracy of 98.89% on the BOT-IOT dataset highlights its robustness and effectiveness in identifying cyber threats. The hybrid approach in cybersecurity is emerging as a strong defense mechanism, showcasing the potential of collaborative, interdisciplinary solutions to combat adversarial challenges in the evolving landscape.