Chaos Engineering is used to optimize MongoDB performance by intentionally introducing failures to test and improve system resilience and query efficiency. This approach addresses common issues such as high CPU usage, disk I/O bottlenecks, and network latency that can cause queries to underperform when deployed to production. By simulating these conditions using tools like Gremlin, developers can establish performance baselines, run chaos experiments, and analyze profiling data to identify potential improvements. Through targeted experiments, such as memory attacks and disk I/O consumption, developers can evaluate and refine their MongoDB configurations to ensure consistent performance under stress. This methodology not only helps in optimizing query execution but also enhances the overall reliability and scalability of distributed database platforms by preparing them for unexpected high-demand scenarios.