The research aims to improve the quality of groundwater level data by utilizing advanced signal decomposition techniques for denoising. Groundwater level measurements can be influenced by environmental noise, sensor errors, and disturbances, potentially concealing true hydrological signals and affecting the accuracy of subsequent analysis. This study compares three popular denoising methods—Wavelet Transform, Singular Spectrum Analysis (SSA), and Variational Mode Decomposition (VMD)—within a Python-based computational setup. The goal is to evaluate how effectively each method removes noise while maintaining critical groundwater level variations. The performance of each technique was evaluated using statistical metrics. The coefficient of determination (R²), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) for both training and testing data. The Wavelet Transform performed better than the other methods, with R² values of 0.982 for training and 0.954 for testing, along with NSE scores of 0.982 and 0.954. VMD showed good results, with R² scores of 0.949 (training) and 0.835 (testing), and NSE values indicating similar accuracy. SSA was less effective at denoising, with R² of 0.906 for training and 0.590 for testing, showing less ability to generalize to new data. These results demonstrate the superiority of the Wavelet Transform in separating noise from important hydrological signals, leading to cleaner groundwater datasets. The denoised data can significantly improve hydrological modeling, forecasting, and groundwater management. Better data quality supports more precise water resource planning, sustainable use, and risk reduction related to groundwater level changes, making these techniques valuable tools for environmental scientists and water resource managers.