Cloud-native system engineering has fundamentally transformed the way modern software applications are architected, deployed, and managed across distributed computing environments. Unlike traditional monolithic models that rely on tightly coupled components and static infrastructure, cloud-native approaches embrace modularity, elasticity, and automation as core design principles. Built around technologies such as containerization, microservices architecture, declarative infrastructure, and automated orchestration, cloud-native systems are specifically engineered to operate efficiently in dynamic public, private, and hybrid cloud ecosystems. These systems are designed not only to scale horizontally in response to fluctuating workloads but also to maintain operational continuity in the presence of hardware failures, network disruptions, and unpredictable traffic surges. A primary objective of cloud-native engineering is to achieve high availability (HA)—ensuring minimal service downtime—and high performance (HP)—delivering low latency, high throughput, and efficient resource utilization. High availability is accomplished through architectural strategies such as redundancy, replication, self-healing mechanisms, intelligent load balancing, and fault isolation. High performance, on the other hand, is supported by horizontal scalability, caching strategies, observability-driven optimization, and automated resource management. Together, these characteristics enable resilient and adaptive distributed systems capable of sustaining mission-critical workloads. This review provides a comprehensive examination of the foundational architectural principles, including microservices decomposition and container orchestration; the enabling technologies that support scalability and resilience; and the operational frameworks that integrate continuous integration and continuous deployment (CI/CD). It further explores advanced performance optimization techniques, such as predictive auto-scaling and edge computing, alongside established resilience strategies, including circuit breaker patterns, chaos engineering, and service mesh architectures. Emphasis is placed on practical design patterns, reliability engineering practices, and the cultural integration of DevOps methodologies to achieve sustained operational excellence. By synthesizing current advancements and emerging trends, this review highlights how cloud-native system engineering is evolving toward autonomous, self-optimizing infrastructures. These infrastructures combine intelligent automation, real-time observability, and predictive resilience to meet the growing demands of large-scale, distributed applications.