Where AI Data Engineering is now shaping and advancing healthcare delivery involves some of the solutions to the problems associated with complex, large and multistructured data systems. This transformation can allow organisations to manage real-time data streams and flows, operate and integrate different systems at once and facilitate the use of high level analytics for better decision making processes. AI acts through the automation of data pipelines to improve the efficiency and efficacy of healthcare service delivery in aspects like; diagnosis, resource allocation & patient care delivery. Therefore, this paper focuses on examining the approaches, uses, and impacts of using artificial intelligence-driven data engineering in healthcare. They argue that potential benefits of telemedicine in cost saving, in terms of scale, and efficiency and improvements in patient care particularly in chronic disease are apparent in the highlighted use cases. The threats like data privacy, the compliance issue, and bias are analyzed, and new opportunities like the federated learning and quantum computing are explored. It enlightens the world with the potential of AI in revolutionizing digital change in healthcare delivery while calling for public policy standards that would safeguard against rampant incorporation of AI in the sector due to its inherent challenges.

Over the last several years edge-cloud data centers have emerged as one of the key infrastructures of contemporary computing enabling real-time computational intensive applications in IoT, Artificial Intelligence, and 5G networks. However, these hybrid environment markers are challenged by issues of resource allocation; they include variability of workloads, resource partitioning, high energy consumption and low operational efficiency. But static and passive methods of resource management are not efficient enough to address the requirements of such systems. These concerns are resolved with AI-driven predictive analytics, which presents the opportunity to predict a company’s resources necessity and further allocate those resources in real-time. In combination with machine learning and deep learning approaches, predictive analytics can calculate workload accurately, optimize energy consumption and identify potential failures at an early stage, which will guarantee efficiency and savings. In this article, the authors discuss the use of predictive analytics in edge-cloud environments, with an emphasis on how this technology facilitates a balance between PUE, throughput, and total power consumption, as well as overall edge-cloud system robustness. Using various AI Informed methods supported by the real-life examples and discussing the technical environments for intelligent technologies, the research also reveals the prospects and issues of the application of such AI systems, such as data heterogeneity, privacy, and scalability. Last but not the least, the discussion indicates that more promising paradigms like federated learning as well as Green-computing principles which suggest future resolution towards the green application of optimal resource utilization in edge cloud intricacies.

Cloud computing has developed at a very fast pace and has transformed data engineering for organizationsto handle big data. But limitations including reliability of data, the ability to expand the cloud based systems,and security become major issues. This paper discusses the use of artificial intelligence (AI) in solving thesechallenges with techniques to improve the reliability of the cloud data engineering. Through the integrationof AI algorithms such as predictive analysis, anomaly detection, and automated optimization, the findings ofthis research highlight how data reliability increases and how the scalability and security compliance of thesystem enhance. Altogether, the research compares AI with the existing literature study, experiments it oncloud platforms, and benchmarks it with traditional approaches to demonstrate how AI can improve dataworkflows, minimize operating expenses, and support better decision making. The results evidence thecapabilities of AI in combination with cloud solutions in establishing effective and progressive dataengineering structures that can advance further as a field.