Cloud-native monitoring and logging techniques have become essential for managing modern distributed applications built on microservices, containers, and dynamic cloud infrastructures. This review examines the evolution of monitoring and logging practices in cloud-native environments, highlighting the shift from traditional system-centric approaches to observability-driven models. It explores key components such as metrics, logs, and distributed traces, which collectively provide comprehensive visibility into system behavior. The study discusses popular tools and frameworks, including Prometheus, Grafana, ELK stack (Elasticsearch, Logstash, Kibana), and OpenTelemetry, which enable real-time monitoring, log aggregation, and analysis. It also emphasizes the importance of centralized logging, automated alerting, and anomaly detection in maintaining system reliability and performance. Furthermore, the review addresses challenges such as data volume management, scalability, latency, and security in handling sensitive log data. Emerging trends, including AI-driven observability, serverless monitoring, and edge-based logging, are also examined. The findings highlight that effective monitoring and logging strategies are critical for ensuring resilience, fault detection, and performance optimization in cloud-native systems.