This paper presents the advantages and disadvantages of using AI-based zero-trust models in cloud environments and the advantages and disadvantages of using artificial intelligence in zero-trust architecture. The zero-trust model is a security architecture based on the “never trust, always verify” concept and has been in greater demand in the cloud due to increased sophistication in cyberattacks. The current study presents the use of AI in enhancing zero-trust efficiency by automating threat detection, access control, and anomaly detection. A literature survey presents the gaps and opportunities in AI usage in zero-trust architecture. The methodology presents machine learning algorithms and accurate data, such as network logs and cloud service records, in AI-based model training and testing. The findings present the significance of enhancing the security of clouds in AI-based zero-trust models but report data quality and scalability challenges. Future directions in research on optimizing the use of these systems are presented.