Accurate prediction of Remaining Useful Life (RUL) for complex industrial machinery is critical for the reliability and maintenance of mechatronic systems, but it is challenged by high-dimensional, noisy sensor data. We propose the Causal-Informed Data Pruning Framework (CIDPF), which pioneers the use of causal inference to identify sensor signals with robust causal relationships to RUL through PCMCI-based stability analysis, while a Gaussian Mixture Model (GMM) screens for anomalies. By training on only 10% of the pruned data, CIDPF fine-tunes pre-trained Large Language Models (LLMs) using parameter-efficient strategies, reducing training time by 90% compared to traditional approaches. Experiments on the N-CMAPSS dataset demonstrate that CIDPF achieves a 26% lower RMSE than existing methods and a 25% improvement over full-data baselines, showcasing superior accuracy and computational efficiency in industrial mechatronic systems. The framework's adaptability to multi-condition scenarios further underscores its practicality for industrial deployment.