<p>Sea Surface Salinity (SSS) is a key indicator of the global water cycle, ocean circulation, and climate variability. This study evaluates SSS variability across the tropical Indian Ocean (TIO) from 1993 to 2015 using four ocean reanalysis datasets (GLORYS2V4, CGLORS, FOAM, and ORAS5) and their ensemble mean (MEAN), with validation against ARGO observations (2005–2015). These reanalyses, based on the Nucleus European Modelling of Ocean (NEMO) framework, differ in model physics, assimilation techniques, and boundary fluxes, resulting in varied representations of SSS. Statistical comparisons show that the MEAN consistently outperforms individual datasets, closely matching ARGO climatology, capturing variability in the Arabian Sea (AS) and Western Equatorial Indian Ocean, and minimizing errors across all regions, including the freshwater-influenced Bay of Bengal (BoB). Harmonic and Fourier analyses reveal strong regional contrasts as the BoB exhibits the highest seasonal and annual variability linked to monsoonal rainfall and river discharge, the AS shows moderate fluctuations driven by evaporation and circulation, while the Eastern and Western Equatorial Indian Ocean (EEIO, WEIO) display weaker and more stable variability. Trend analysis using the Mann–Kendall test indicates significant freshening in the BoB only in ORAS5, with other datasets showing weaker or inconsistent signals, and mixed salinification patterns in the AS. Spectral analyses confirm the dominance of the annual cycle, with semi-annual contributions in coastal and equatorial zones, and reduced uncertainty after 2000. Overall, this multi-model assessment highlights the value of ensemble approach in reducing uncertainty, improving robustness, and enhancing confidence in monitoring SSS variability across the TIO.</p>