This paper explores the application of artificial intelligence (AI) in resource management for cloud computing systems, services, and applications. It reviews the challenges in cloud computing, such as scalability, heterogeneity, quality of service (QoS), and cost optimization, and discusses various AI techniques including machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. The paper outlines specific AI-based strategies for efficient resource management, such as automated resource provisioning, intelligent workload planning, predictive maintenance, and energy-efficient resource management. It also presents case studies and applications in different cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. The paper emphasizes the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems and discusses the integration of AI into existing resource management frameworks. Finally, it highlights future directions, including real-time resource optimization and coordination.This paper explores the application of artificial intelligence (AI) in resource management for cloud computing systems, services, and applications. It reviews the challenges in cloud computing, such as scalability, heterogeneity, quality of service (QoS), and cost optimization, and discusses various AI techniques including machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. The paper outlines specific AI-based strategies for efficient resource management, such as automated resource provisioning, intelligent workload planning, predictive maintenance, and energy-efficient resource management. It also presents case studies and applications in different cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. The paper emphasizes the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems and discusses the integration of AI into existing resource management frameworks. Finally, it highlights future directions, including real-time resource optimization and coordination.