The rapid adoption of cloud-native applications, automation pipelines, artificial intelligence services, containers, serverless workloads, and machine-to-machine communication has substantially increased the number and operational importance of non-human identities (NHIs). Unlike human identities, NHIs frequently operate continuously, interact across distributed environments, and may possess permissions that are difficult to observe, evaluate, and revoke dynamically. Consequently, conventional identity and access management approaches based primarily on static credentials and manually administered permissions are insufficient for highly automated cloud environments. This research proposes a Zero-Trust Framework for Automated Protection of Non-Human Identities in Cloud IAM. The study follows a conceptual research-and-review methodology based exclusively on the supplied literature and synthesizes principles from automated governance, distributed computing, machine learning, optimization, DataOps, IoT, and cyber-physical systems. The proposed framework combines continuous identity discovery, contextual authentication, least-privilege authorization, behavioral monitoring, risk scoring, automated credential governance, policy optimization, and continuous revocation. The analysis indicates that NHI protection should be treated as a continuously optimized governance process rather than a one-time access-control configuration. The framework further establishes a feedback mechanism through which observed identity behavior can influence future authorization decisions. The research contributes a structured conceptual architecture for integrating Zero Trust principles with automated NHI lifecycle management and identifies implementation challenges involving policy complexity, false positives, interoperability, explainability, and operational scalability.