• Synthesis of Ag 2 Se–PVA capped nanoparticles through sol-gel method • TEM images shows the improved shape and sizes of the particles when the stabilizer was used • Optimum pH of 3 was recovered for all adsorbents. • Absorption of the chromium ions was achieved through amino functional groups (-NH 2 ) and the hydroxyl functional groups (–OH) active sites. • Highest percentage removal of above 87 % of Cr(VI) ions was achieved when PVA-Ag 2 Se-chitosan nanocomposites was used as an adsorbent. Silver selenide nanoparticles capped with polyvinyl alcohol were synthesized and incorporated with chitosan to form PVA-Ag 2 Se-Chitosan nanocomposites. The polymer nanocomposites were utilized to remove Cr(VI) ions from wastewater through adsorption process. Ultraviolet-visible (UV-Vis) spectroscopy, X-ray diffraction, Fourier transforms infrared (FTIR), transmittance electron microscopy (TEM), scanning electron microscope (SEM), and Brunauer–Emmett–Teller (BET) were used to characterize the nanoparticles and the nanocomposites. TEM images of PVA-Ag 2 Se showed nicely packed spherical particles as the stabilizer was used with the average particle of 3.65±1.36 nm. BET surface area and the average pore size of PVA-Ag 2 Se-Chitosan nanocomposite are 3.28 (m 2 /g) and 10.73 nm, respectively. Adsorption of chromium (VI) onto chitosan and PVA-Ag 2 Se-Chitosan nanocomposite was investigated using atomic adsorption spectroscopy (AAs). The parameters such as contact time, pH and initial metal concentration were investigated during this process. The results showed that PVA-Ag 2 Se-Chitosan nanocomposite is a promising candidate as a new generation adsorbent as it displayed high percentage removal of Cr(VI) ions than pure chitosan. The crucial parameters of Langmuir and Freundlich adsorption isotherms were successively applied to analyze the adsorption mechanism. The adsorption of hexavalent chromium is better described by the Langmuir model (R 2 = 0.9683) than by the Freundlich model (R 2 = 0.8782).

• Experimental optimization required validation; MCDA was used to confirm robustness of key parameters. • Application of MCDA enables structured, multi-dimensional analysis for optimizing hydrate desalination parameters. • Pioneered the use of MCDA to systematically optimize hydrate desalination process parameters. • Establishes a foundation for integrating AI-enhanced MCDA frameworks in adaptive and resilient desalination systems. • Future research could improve real-time, data-driven MCDA in the dynamic gas hydrate desalination systems. Gas hydrate-based desalination (GHBD) is a promising technology for sustainable water treatment, yet its practical implementation is often hindered by the slow kinetics and complexity of optimizing multiple process parameters. This study develops a robust decision-making framework using multi-criteria decision analysis (MCDA) to identify optimal conditions specifically volume, pressure, and stirring speed that enhance water recovery (WR) and moles of gas consumed. Experimental data were evaluated through MCDA based ranking methods to assess parameter performance. The results indicate that for CO₂ hydrate formation, the optimal conditions, 500 mL, at 3.0 MPa, provided a highest rank of 93 and stirring speed of 450 rpm with a rank of 32, produced a WR of 50%. In contrast, for CO₂+ C ₃H₈ hydrate systems in treating produced water (PW), at 2.0 MPa yielded the best performance with a highest rank of 39 and a WR of ∼60%. Unlike previous GHBD studies that primarily focus on feasibility and experimental characterization, this work introduces the first systematic MCDA based optimization framework for GHBD and provides experimentally validated optimal operating conditions. These findings highlight the importance of precise parameter selection and confirm the effectiveness of MCDA in guiding decision making for GHBD. This work introduces the first MCDA based framework for systematically optimizing operating parameters in GHBD. It uniquely shows that MCDA can reliably identify optimal CO₂ and CO₂ + C ₃H₈ hydrate conditions, WR, efficient scalable desalination strategies, supporting long term environmental sustainability. and scalability of GHBD.