Despite its benefits, synthetic data brings challenges around bias, overfitting, and accuracy. If not carefully designed, it can reflect unrealistic patterns that harm AI performance. Ensuring the quality of synthetic datasets means testing them against real-world benchmarks, reducing algorithmic bias, and constantly improving simulation realism through feedback loops.