Attestation is critical in verifying the integrity, authenticity, and reliability of IoT devices, especially those managing sensitive data such as wearables, industrial IoT devices, and servers. These devices are increasingly vulnerable to threats like edge-based attacks, denial-of-service (DoS), counterfeiting, and firmware tampering. Current attestation mechanisms often fall short in addressing lifecycle management and large-scale deployments effectively. This paper proposes an advanced attestation framework integrating federated learning and explainable AI (XAI) techniques using neural networks. With a predictive accuracy of around 99%, this framework allows for enhanced transparency, interpretability, and robustness in IoT security. Federated learning enables distributed training across IoT devices, while XAI provides detailed insights into the model’s decision-making process for validating attestation results. This novel approach supports comprehensive lifecycle management and scalable network deployments, significantly advancing IoT device verification and security.