Compiler defects, especially optimization defects, pose significant threats to software systems. Diagnosing their root causes is crucial for efficient debugging and fixing but remains highly challenging due to complicated interactions of optimizations and ambiguity in root cause prediction. To address these challenges, in this paper, we propose a novel approach, which leverages a data-driven approach and the strengths of Large Language Models (LLMs) for automated root cause analysis and prediction. Specifically, we first extract and iteratively summarize the debugging information from historically fixed optimization defects, and ultimately construct a systematic taxonomy with eight root cause categories. Then, based on this taxonomy, we propose SRLRF, which leverages a domain-specific LLM (i.e., RTA) and a general-purpose LLM (i.e., Lllama3.1) to achieve multiple root cause categories’ prediction. Finally, SRLRF integrates stacking ensemble learning to train the prediction model to improve prediction performance. The experimental results on 5,573 GCC optimization defects show that SRLRF is able to accurately predict the root causes of 63.11% optimization defects, and significantly outperforms four baselines with the average improvements ranging from 16.98% to 179.99%.