Abstarct: In recent decades, growing global concerns over energy insecurity, freshwater scarcity, and environmental degradation have highlighted the urgent need for sustainable, integrated systems capable of simultaneously producing electricity, clean water, and green fuels. In this context, the present study investigates the design, modeling, performance evaluation, and economic analysis of a geothermal-baxsed poly-generation system. The proposed configuration integrates four key subsystems: two Organic Rankine Cycles (ORC) utilizing R113 and R245fa as working fluids, a Zero Liquid Discharge (ZLD) system , and a Proton Exchange Membrane (PEM) electrolyzer for green hydrogen production. In the proposed system, geothermal energy at approximately 180°C is initially used in the first ORC to generate electricity. The residual heat transferred to the ZLD unit. The concentrated brine from the MED system is directed to the crystallizer unit, recover high-purity water and solid salt. the purified water and a portion of the electricity produced are supplied to the PEM electrolyzer to generate high-purity hydrogen. Finally, the remaining energy from the geothermal water is recovered in a secondary ORC. The modeling process was carried out using steady-state thermodynamic relations, estimating energy, mass, and cost, and the results were presented separately for each section in the form of graphs and tables. baxsed on the results obtained in this research, the system in question is capable of generating about 2.7 MW of net electricity in the baxse scenario. The output of the ZLD unit is estimated to be 112.1 m3/day of fresh water. Furthermore, the PEM electrolyzer can generate 218.1 kg/day of high-purity hydrogen. From an economic perspective, the investment cost of the entire system is estimated at about 3.6 million USD and the payback period is reported to be less than 2 years. Also, analysis of the results indicates that the temperature of the ZLD heat source, the working pressure of the turbines, and the electrolysis current density are the most important parameters affecting the overall performance of the system, which should be given special attention in the optimization process.