The rapid expansion of cloud computing has introduced significant security challenges, including data breaches, insider threats, misconfigurations, and advanced persistent attacks. Traditional security mechanisms are often insufficient to address the dynamic and large-scale nature of cloud environments. Artificial intelligence (AI) has emerged as a powerful approach to enhance cloud security by enabling intelligent, adaptive, and automated threat detection and response. This study explores AI-driven security solutions for cloud computing, focusing on how machine learning, deep learning, and data analytics techniques are applied to identify anomalies, predict potential threats, and strengthen overall system resilience. The paper examines key AI-based security mechanisms such as intrusion detection systems, behavioral analytics, threat intelligence, and automated incident response. It also discusses the integration of AI with cloud security frameworks, including zero-trust architecture, security information and event management (SIEM), and cloud security posture management (CSPM). Furthermore, the study highlights challenges such as data privacy, model accuracy, adversarial attacks, and scalability limitations. Emerging trends such as explainable AI, federated learning, and AI-powered autonomous security systems are also analyzed. The findings emphasize that AI-driven security solutions significantly enhance the ability to protect cloud environments, ensuring confidentiality, integrity, and availability of data and services.