Analysis of the Integration of Artificial Intelligence Technologies in Cloud Computing Management

Introduction: Automation of predictive analytics—a subset of AI—has gained more attention in addressing unresolved challenges related to energy efficiency in IT and datacenter domains. The increasing reliance on cloud computing and data-intensive applications necessitates innovative AI-driven solutions for optimizing resource utilization. As modern ICT solutions integrate AI methods, tools, and techniques, new opportunities and challenges emerge. This study explores the role of blockchain, IoT, and AI in shaping the future of cloud computing, emphasizing energy efficiency and sustainability. Objectives: The primary objective of this study is to assess the impact of AI, IoT, and blockchain on cloud computing evolution. Specifically, the research aims to: Analyze how AI-driven automation improves energy efficiency in cloud and datacenter environments. Review existing AI-based predictive models used for energy consumption optimization. Identify open challenges and research directions in AI-powered cloud solutions. Methods: To achieve the objectives, a systematic review of research papers related to cloud computing paradigms and emerging technologies is conducted. The collected studies are categorized based on their contributions to energy optimization, AI-based predictive modeling, and anomaly detection. The analysis includes experimental findings from various AI models, particularly those utilizing LSTM and other machine learning techniques for energy forecasting. Results: Several AI-based methodologies have demonstrated effectiveness in reducing energy consumption and carbon footprints in cloud and data-center environments. Key findings include: AI-driven prediction models successfully mitigating resource-intensive activities. The development of AI systems capable of detecting and addressing energy anomalies. The use of LSTM-based models for precise daily energy consumption forecasting. Enhanced cloud resource management through AI-powered automation. Conclusions: AI-driven automation holds significant potential for optimizing energy efficiency in cloud computing. The integration of predictive analytics, IoT, and blockchain can revolutionize modern ICT infrastructures. Future research should focus on refining AI models, improving anomaly detection techniques, and enhancing sustainability through intelligent cloud management solutions. This study provides a foundation for prospective research in AI-powered cloud computing, highlighting key challenges and opportunities.