Detecting intrusions, ensuring effective operation, autono-mous response, and continuous monitoring present significant challenges for the widespread adoption of the Internet of Things (IoT). Recent research has delved into incorporating machine learning techniques, such as Hidden Hierarchical Markov Models (HHMM), to imbue IoT networks with context-aware self-healing capabilities, aiming to tackle these obstacles. These investigations underscore the pivotal role of context-aware and automated intrusion detection systems (IDS) in identifying and mitigating security vulnerabilities within IoT environments. In addition, recent studies have concentrated on creating self-healing methodologies capable of dynamically adjusting response plans, thus diminishing human intervention and ameliorating real-time security concerns. Such autonomous response capabilities are indispensable for enhancing the security, resilience, and autonomy of IoT systems. To address these imperatives, this article introduces context-aware self-healing mechanisms leveraging HHMM, machine learning algorithms, cybersecurity methodologies, and standardized self-healing protocols. The proposed approach involves the development of a monitoring application that autonomously gathers system information, applies our detection strategy, and adapts to evolving network conditions over time. The experimental validation conducted on our platform shows promising results, affirming the efficacy and viability of the proposed solution. This comprehensive approach promises to fortify IoT systems against emerging threats, enhancing their adaptability and robustness in dynamic environments.