The Gulf Cooperation Council (GCC) nations face pressing challenges in balancing water and energy demand under conditions of rapid growth and harsh climates. This study develops a machine learning-driven framework that combines forecasting and optimization to support sustainable resource management in Oman, Saudi Arabia, and the United Arab Emirates using data from 2010 to 2022. Three forecasting models-Long Short-Term Memory (LSTM), Prophet, and ARIMA-were evaluated, with LSTM showing superior performance by reducing root mean square error (RMSE) by 18-22%. An optimization module based on a Genetic Algorithm (GA) was then applied, achieving reductions of 10-12% in peak load stress and improvements of 15-20% in water reuse efficiency. These findings provide practical guidance for policymakers and align with national strategies such as Oman Vision 2040 and global commitments under the United Nations Sustainable Development Goals (SDGs) 6 and 7. The proposed framework offers a scalable pathway toward sustainable water and energy management in the GCC region.

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