The rapid evolution of cloud computing paradigms has significantly accelerated the adoption of distributed, cloud-native systems grounded in microservices architecture, containerization, dynamic orchestration, and continuous delivery pipelines. Unlike traditional monolithic systems that rely on tightly coupled components and static infrastructure, cloud-native applications are deliberately engineered to leverage elastic scalability, resource abstraction, and automated infrastructure provisioning within highly dynamic cloud environments. Foundational platforms such as Docker and Kubernetes have enabled the development of portable, resilient, and self-healing workloads capable of operating consistently across heterogeneous infrastructures. These technologies facilitate container image standardization, declarative orchestration, automated scaling, and fault recovery. However, as deployments extend to multi-cluster, hybrid-cloud, and multi-cloud ecosystems, system complexity increases exponentially, making comprehensive lifecycle governance a significant technical and organizational challenge. End-to-end lifecycle management therefore encompasses not only architectural design and containerized development but also automated CI/CD pipelines, runtime orchestration, observability engineering, security integration, performance tuning, cost governance (FinOps), and systematic service decommissioning. This review synthesizes contemporary methodologies, architectural patterns, and operational frameworks that support lifecycle governance within large-scale cloud-native ecosystems. It critically examines cross-cutting paradigms including DevSecOps integration, Infrastructure as Code (IaC), GitOps workflows, service mesh architectures, policy-as-code enforcement, FinOps optimization, and AI-driven operations (AIOps). These paradigms collectively emphasize automation, declarative configuration management, continuous validation, and compliance-aware deployment strategies. Particular attention is devoted to runtime observability engineering, integrating metrics, logs, and distributed tracing to enable proactive monitoring and rapid fault isolation. Additionally, the review addresses emerging security imperatives such as software supply chain integrity, container image signing, zero-trust networking models, and runtime threat detection. By embedding governance mechanisms directly into CI/CD and orchestration pipelines, organizations can mitigate configuration drift, reduce operational risk, and enhance resilience in highly dynamic distributed environments. Furthermore, emerging directions such as platform engineering, internal developer platforms (IDPs), serverless-native orchestration models, eBPF-based deep observability, and autonomous remediation frameworks are analyzed as transformative drivers of next-generation lifecycle management. These innovations aim to abstract operational complexity, improve developer productivity, and enable predictive, self-optimizing infrastructure behavior. The study concludes that holistic lifecycle integration—rather than isolated adoption of discrete tools—is essential for achieving sustained operational resilience, regulatory compliance, energy-efficient infrastructure utilization, and continuous innovation in large-scale distributed ecosystems. By consolidating architectural principles, operational best practices, and forward-looking research trajectories, this review provides a comprehensive conceptual and practical framework for researchers and practitioners seeking to advance end-to-end lifecycle management strategies in modern cloud-native systems.