Pressure-driven membrane processes, such as reverse osmosis and nanofiltration, represent credible processes for salinity reduction in ground, surface, and seawater, as well as in mining and urban wastewater. The separation characteristics and productivity of these processes depend on several factors, including molecular weight cut-off and operating conditions (applied pressure, recovery rate..). This study aims to model salt rejection performance of water in Tan-Tan City (Morocco) using nanofiltration membranes (NF90, NF200, NE90) and reverse osmosis membranes (BW30LE), under various operational conditions, used the Spiegler-Kedem model. Both the Particle Swarm Optimization and Grey Wolf Optimization algorithms were applied to optimize the model parameters to fit experimental data. The results showed excellent agreement between experimental rejection rates and model-predicted rejection rates for both algorithms. Additionally, Grey Wolf Optimization model gave slightly better results compared to Particle Swarm Optimization. The combined use of a well-established theoretical framework and efficient optimization algorithms provides a significant step forward in the quest for reliable and sustainable water resources.