<p>Cloud computing and relational database systems provide the best possible combination of scalability, dependability, and performance. However, cloud developers often face deployment and design challenges when migrating from legacy systems to modern cloud-native architectures. Although relational databases are provided by Amazon Web Services Relational Database Service (AWS RDS), full utilization requires well-considered design patterns and effective deployment techniques. Building upon previous research, this paper examines advanced design patterns tailored for cloud-native relational databases and suggests a new design pattern, Hybrid Consistency and Adaptive Query Coordination (HC-AQC), to tackle these issues. Cloud-native systems can handle different consistency requirements thanks to the pattern's flexibility, fault tolerance, and performance optimization. This will help the system change its rules for how it handles tasks and queries based on health indicators and the importance of the operations. This ensures that different types of work are managed efficiently. This pattern will eliminate performance bottlenecks and foster a context where high-priority data is accurate and consistent. We will apply performance comparison analysis to these patterns and strategies within AWS RDS to understand their scalability, throughput, fault tolerance, and availability. These patterns, studied thoroughly in this paper, allow actionable insights for software architects, developers, and database administrators to develop robust, scalable, and resilient web applications based on AWS RDS. Experimental results indicate that the proposed patterns perform better than traditional approaches on overall scalability, latencies, and trustworthiness of operations, thus providing a groundbreaking development in optimizing database architecture in cloud settings. The findings give some useful insights into designing resilient, high-performance database systems in AWS RDS, impacting the cloud-native database architecture.</p>