Intelligent enterprise systems now operate in environments with constant cyber risk, distributed workloads, and high data mobility. These systems depend on artificial intelligence, automation, and cloud-native architectures, yet their attack surfaces grow as connectivity increases. Zero Trust provides a security model built on continuous verification, least-privilege access, micro-segmentation, and real-time monitoring. This study evaluates how Zero Trust principles improve the security, resilience, and operational performance of intelligent enterprise systems. The paper reviews empirical and theoretical research from cloud security, identity management, distributed systems, and AI-enabled defense. Findings show that Zero Trust reduces breach probability, strengthens identity assurance, improves visibility across hybrid infrastructures, and supports adaptive access decisions in automated environments (Roseet al., 2020; Alshamraniet al., 2019; Hashimet al., 2022). Evidence also shows that intelligent systems require stronger controls due to opaque ML pipelines, model poisoning risks, and expanded API surfaces (Pitropakiset al., 2019; Kumaret al., 2023). Zero Trust mitigates these risks by enforcing granular identity policies, continuous device trust scoring, encrypted east-west traffic, and autonomous threat detection. However, the study identifies adoption challenges, including integration complexity, legacy system constraints, identity sprawl, and increased computational cost in large enterprise environments (Teixeiraet al., 2022; NIST SP 800-207). The paper concludes that Zero Trust improves system accountability, reduces attack propagation, and enables secure automation when supported by strong identity governance, robust telemetry pipelines, and mature cloud architectures. The study provides a validated framework for aligning Zero Trust capabilities with intelligent enterprise system design.