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This study explores strategies to maximize salinity gradient power (SGP) generation using reverse electrodialysis (RED), focusing on key operating parameters under Swedish environmental conditions. Herein, using a full-factorial experimental design, seawater salinity, flow velocities, and water temperature is varied across three levels to assess their impact on SGP output. machine learning methods predict power density (PD), including 1) ensemble learning with decision tree (DT), 2) gaussian process regression (GPR), and 3) artificial neural network (ANN). Fivefold cross-validation confirms the ANN's high accuracy (root mean squared error (RMSE): 1.173%, R2: 99.35%), closely followed by GPR (RMSE: 1.95%, R2: 99.17%). A feature and trend pattern analysis among the input factors reveals sea salinity as the primary influence on PD, with temperature as the secondary contributor. Complementing this, a life cycle assessment examines the environmental impact of RED systems, identifying the Seawater River RED and brine-wastewater treatment plant RED systems as having environmental effects, particularly on ozone layer depletion and freshwater toxicity. Carbon fiber-based (CF) electrodes, especially lignin CF, demonstrate a lower impact, yet concerns remain over key sustainability challenges. These findings highlight SGP's potential as a viable renewable source, highlighting areas for future material selection and system efficiency improvements.