Satellite radar altimetry has significantly enhanced inland water monitoring by providing consistent, long-term lake-level observations. However, these datasets often contain substantial gaps caused by sensor malfunctions, orbital limitations, or retrieval errors, complicating hydrological analyses and downstream applications. This study introduces a robust benchmarking framework designed to systematically evaluate gap-filling techniques in satellite-derived lake water-level records through controlled pseudo-gap injection experiments. <br />We investigated three large lakes with distinct hydrological behaviours, the Caspian Sea, Lake Superior, and Lake Tanganyika each representing unique temporal dynamics. Synthetic seven-year gaps (2002–2008) were artificially introduced into complete altimetry datasets, and three sophisticated gap-filling methods were compared: Singular Spectrum Analysis (SSA), Bidirectional Autoregressive (BIAR) models, and Bidirectional Multi-Layer Perceptrons (BiMLP). Method performance was assessed using standard metrics (RMSE, MAE, Bias, and R<sup>2</sup>), alongside statistical properties including variance, skewness, autocorrelation, and stationarity. <br />BiMLP consistently delivered the highest accuracy across all study lakes, demonstrating exceptional adaptability to both smooth and highly variable signals. SSA performed effectively for lakes exhibiting quasi-periodic behavior, while BIAR showed sensitivity to lag selection and reduced performance under non-stationary conditions. These results emphasize the critical role of bidirectional modeling approaches and rigorous time-series diagnostics in selecting appropriate gap-filling methods for satellite altimetry-based hydrological studies. These findings provide practical guidance for selecting appropriate gap-filling strategies under long data outages in satellite altimetry workflows.