Data consistency is key to distributed cloud databases. Multiple nodes store and process data in these databases. Distributed systems in cloud computing have increased the need to evaluate consistency models, each with pros and cons in terms of performance, scalability, and fault tolerance. This study contrasts four data consistency models: strong consistency, eventual consistency, causal consistency, and hybrid approaches using secondary data from previous studies, benchmarks, and real-world applications. The analysis framework evaluates availability, latency, throughput, and network partition resilience to determine the best model. Due to latency and scalability issues, stadiums, airports, and financial institutions aren’t good places to implement strong consistency, according to research. Strong consistency ensures absolute reliability. However, social media platforms and e-commerce websites may experience temporary data anomalies because eventual consistency prioritizes availability and scalability. Application-specific optimizations in hybrid models aim to balance these trade-offs. This study benchmarks data consistency models in distributed cloud databases and measures latency, throughput, and fault tolerance to help system architects. It also highlights practical uses for consistency models, highlights knowledge gaps in IoT and edge computing consistency issues, and suggests ways to improve consistency mechanisms. This study emphasizes the importance of database architects and developers considering application requirements when choosing consistency models. Future research should improve compliance in new cloud environments and hybrid models.