###### Abstract  
This paper presents an in-depth exploration of the transformative impact of integrating the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) within the domain of aviation maintenance. It articulates the transition from conventional health monitoring practices to a more advanced, comprehensive health management approach, leveraging these modern technologies. This paper emphasizes the pivotal shift from reactive maintenance strategies to proactive and predictive maintenance paradigms, facilitated by the real-time data collection capabilities of IoT devices and the analytical prowess of AI. This transition not only enhances the safety and reliability of flight operations but also optimizes maintenance procedures, thereby reducing operational costs and improving efficiency. This paper meticulously outlines the implementation challenges, including technological integration, regulatory compliance, and security concerns, while proposing a future research agenda to address these issues and further harness the potential of these technologies in revolutionizing aviation maintenance. IoT; AI; aircraft; health monitoring; health management; aviation maintenance +
Footnote †: journal:  
ing potential issues early and enabling maintenance actions to be taken before problems arise, these technologies ensure that aircraft are in optimal condition for safe operation. Furthermore, the ability to predict and prevent failures reduces the likelihood of in-flight malfunctions, significantly contributing to the overall safety of air travel. The aviation industry's transition from traditional health monitoring (HM) systems to more comprehensive health management (HMGT) approaches represents a pivotal shift towards predictive and proactive maintenance strategies [1]. This evolution is not just a matter of semantic difference but signifies a deeper transformation in how aircraft health is approached, leveraging cutting-edge technologies like the IoT and AI. The current shift towards health management extends beyond monitoring, aiming for a holistic view of an aircraft's operational readiness and lifecycle management. It encompasses predictive maintenance, condition-based monitoring, and even extends into logistics and supply chain management for parts and repairs. This approach demands not just data collection but deep, insightful analysis and strategic planning, areas where the IoT and AI are poised to make a significant impact. One of the primary challenges for researchers is fully understanding the capabilities and limitations of the IoT and AI within the health management paradigm. IoT devices offer unprecedented data collection opportunities, but the sheer volume and variety of data can overwhelm traditional processing and analysis methods. Similarly, while AI has the potential to derive meaningful insights from these data, the complexity and unpredictability of aircraft systems and operations introduce significant challenges in model accuracy and reliability. The transition to health management also brings new problems to the forefront, such as predicting component lifespan, optimizing repair and maintenance logistics, and even anticipating regulatory and environmental changes. Researchers must explore how the IoT and AI can contribute to these areas, requiring a multidisciplinary approach that spans engineering, data science, logistics, and policy studies. The overarching goal of this paper is to investigate and develop a comprehensive framework for integrating IoT and AI technologies into aviation health management systems. This framework aims to address the challenges posed by the transition from traditional health monitoring to advanced health management approaches, enhancing predictive maintenance, operational efficiency, and the safety of flight operations. The paper is organized as follows: Section 2 delineates the research methodologies and analytical procedures employed in this study.