Efficient separation of water from crude oil emulsions is a critical challenge in petroleum production because stable emulsions can reduce production efficiency, increase transportation costs, promote corrosion, and negatively affect refinery operations. Therefore, the development of effective and optimized demulsification strategies is essential for improving crude oil processing and produced-water management in oilfield facilities and crude oil dehydration units. In this study, a hybrid nanocomposite demulsifier based on SiO₂ nanoparticles and trioctylmethylammonium chloride (TOMAC) was developed and evaluated through laboratory experiments combined with machine learning (ML) modeling and optimization techniques. Bottle tests were first conducted to determine the optimal mixing ratio of SiO₂ nanoparticles and TOMAC under controlled conditions. The results showed that a 1:3 (SiO₂:TOMAC) ratio provided the highest demulsification performance and was therefore selected for further investigation. A total of 192 experimental runs were performed by varying key operational parameters, including temperature (25–90 °C), water content (5–45%), salinity (10–30 g NaCl/L water), and demulsifier dosage (20–80 ppm). The maximum water separation efficiency (WSE) obtained experimentally reached approximately 96%. To predict and analyze the demulsification performance, four machine learning models—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM)—were developed. Among them, the SVM model demonstrated the highest predictive accuracy with an R² of 0.99998, mean absolute error (MAE) of 0.04026, and mean squared error (MSE) of 0.01233. Correlation analysis indicated that demulsifier dosage and temperature were the most influential variables, with Spearman correlation coefficients of 0.83 and 0.43, respectively. To further enhance the demulsification process, the SVM model was integrated with Particle Swarm Optimization (PSO