The swift integration of Machine Learning (ML) in Internet of Things (IoT) networks has brought forth intelligent systems capable of performing tasks such as intrusion detection, malware identification, and device authentication. However, this collaboration between IoT and ML has also made these systems more susceptible to critical vulnerabilities—most notably, adversarial attacks that manipulate ML models through the introduction of carefully crafted perturbations. These attacks can undermine the reliability of IoT security solutions, resulting in misclassification, unauthorized access, and operational disruption. This work presents an in-depth overview of Adversarial Machine Learning (AML) in the context of the Internet of Things. It evaluates state-of-the-art ML-based security paradigms, reviews major attack generation techniques, and offers a systematic taxonomy of adversarial threats. In addition, it introduces a new hybrid safety strategy that adds cloud-based GAN analysis with a rule-based identity on the network team. It is easy at calculation overhead that improves the defence accuracy at several levels and sets a solid platform for distribution in the real IoT context. In addition to the observation of recent development (2020–2024), the work also emphasizes significant assessment challenges and maps future roads against craft efficient and flexible AML rescue.