This paper explores how deep learning enhances Internet of Things (IoT) cybersecurity, examining advanced methods like convolutional and recurrent neural networks for detailed IoT data analysis. It highlights the importance of real-time threat detection and classification, focusing on innovative Graph Neural Networks and Transformer Models for better network security. The study also considers Federated Learning and Edge Computing for decentralized, privacy-friendly data handling, and Explainable AI for clarity in decision-making. It addresses the growing challenges of creating scalable, adaptable deep learning models for ever-changing IoT environments and cyber threats, emphasizing the need for ongoing research in developing resilient IoT cybersecurity solutions. The analysis further reveals that deep learning techniques are increasingly effective in anomaly detection and predictive maintenance, reducing false positives, and adapting to new types of cyber threats dynamically. Specifically, it emphasizes how Transformer Models and Graph Neural Networks offer promising results in contextualizing and mitigating complex multi-stage cyber-attacks, enhancing the robustness of IoT systems against evolving threats.