This study presents a comprehensive investigation of the main parameters affecting petroleum emulsion stability and separation efficiency through systematic experimental assessment and statistical modeling. Response Surface Methodology (RSM) was employed to design and analyze experiments, evaluating the effects of demulsifier concentration, temperature, and water content on demulsification efficiency (DME). The bottle test methodology was utilized to assess the separation performance under various designed operational conditions. Statistical analysis through analysis of variance (ANOVA) led to the development of a reduced quadratic model with exceptional predictive accuracy (R² = 0.9923 and adjusted R² = 0.9865), demonstrating high statistical significance (P-value < 0.0001, F-value = 170.95). A key contribution of this research is the development of a new model that accurately predicts demulsifier performance and determines the influence of various parameters on the demulsification process. Parametric analysis demonstrated that elevated temperatures and increased demulsifier concentrations significantly enhanced DME performance, whereas water content exhibited minimal influence on the separation efficiency. Increasing temperature (20 to 80°C) improved efficiency from 35 to 72%, higher demulsifier concentration (25 to 75 ppm) raised it from 28 to 80%, and greater water content (5 to 35%) increased it from 58 to 68%. Furthermore, the optimization process identified optimal conditions at 74 ppm demulsifier concentration, 76°C temperature, and 30 vol% water content, achieving a maximum demulsification efficiency of 95.22%. This study provides valuable insights for industrial applications in oil processing and establishes a robust framework for optimizing the emulsion separation processes.