This paper discusses AI-driven resource management strategies for cloud computing systems, services, and applications. Cloud computing has transformed how resources are accessed and used, but efficient resource management remains a challenge due to scalability, heterogeneity, and dynamic nature of these environments. AI technology is emerging as an effective solution to improve resource management efficiency. The paper reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. It outlines various AI techniques used for resource management, such as machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. The paper considers specific AI-based strategies for efficient resource management, including automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. It presents case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. The paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including real-time resource optimization and coordination. The paper also discusses fog computing, edge-cloud systems, and intelligent cloud computing systems. Fog computing is a distributed computing infrastructure that reduces the strain on resources by processing data closer to the source. Edge-cloud systems distribute resource functions such as computing power, networking, and storage closer to the traffic source. Intelligent cloud computing systems use AI to manage computing resources, including dynamic resource prediction and allocation in 5G cloud radio access networks. The paper concludes that AI-driven resource management strategies have the potential to revolutionize cloud computing systems, services, and applications by optimizing resource utilization, improving performance, increasing scalability, and strengthening security through intelligent decision-making, predictive analytics, and proactive resource allocation.This paper discusses AI-driven resource management strategies for cloud computing systems, services, and applications. Cloud computing has transformed how resources are accessed and used, but efficient resource management remains a challenge due to scalability, heterogeneity, and dynamic nature of these environments. AI technology is emerging as an effective solution to improve resource management efficiency. The paper reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. It outlines various AI techniques used for resource management, such as machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. The paper considers specific AI-based strategies for efficient resource management, including automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. It presents case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. The paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including real-time resource optimization and coordination. The paper also discusses fog computing, edge-cloud systems, and intelligent cloud computing systems. Fog computing is a distributed computing infrastructure that reduces the strain on resources by processing data closer to the source. Edge-cloud systems distribute resource functions such as computing power, networking, and storage closer to the traffic source. Intelligent cloud computing systems use AI to manage computing resources, including dynamic resource prediction and allocation in 5G cloud radio access networks. The paper concludes that AI-driven resource management strategies have the potential to revolutionize cloud computing systems, services, and applications by optimizing resource utilization, improving performance, increasing scalability, and strengthening security through intelligent decision-making, predictive analytics, and proactive resource allocation.