Accurate water quality prediction is paramount for the sustainable management of surface water resources. Current deep learning models face challenges in reliably forecasting water quality due to the non-stationarity of environmental conditions and the intricate interactions among various environmental factors. This study introduces a novel, multi-level coupled machine learning framework that integrates data denoising, feature selection, and Long Short-Term Memory (LSTM) networks to enhance predictive accuracy. The findings demonstrate that the LSTM model incorporates data denoising pre-processing, capturing non-stationary water quality patterns more effectively than the baseline model, enhancing prediction performance (R