The Nanpu Sag in the Bohai Bay Basin is one of China's key areas for oil and gas exploration, noted for its rich hydrocarbon resources and complex reservoir structures. Volcanic reservoirs in this region are influenced by various factors such as lithofacies, lithology, and types of reservoir space, making the prediction of fluid distribution a considerable challenge. This study explores the application of machine learning techniques, particularly the Support Vector Machine (SVM) algorithm, in predicting fluid properties within the volcanic reservoirs of the Nanpu Sag in the Bohai Bay Basin. The research blocks are 1–5 blocks and 1–1 blocks. The research findings are as follows: (1) Advantages of SVM Algorithm: SVM algorithms map data into a high-dimensional space to find the optimal hyperplane for classification, showing significant advantages in dealing with high-dimensional and nonlinear problems. This is particularly beneficial in scenarios involving small samples, nonlinearity, and multi-class classification issues, where SVM exhibits outstanding performance. (2) Key Parameters for Fluid Prediction: Analysis of logging response characteristics in the Nanpu Sag indicates that single-information parameters such as acoustic transit time, compensated density, resistivity, and natural gamma relative value are critical. Additionally, multi-information fusion parameters like the total hydrocarbon ratio, hydrocarbon density index, and hydrocarbon moisture index are also vital. These seven parameters are essential for fluid prediction and are incorporated into the model development process. (3) Fluid Property Prediction Using SVM: The SVM algorithm was applied in experiments to predict fluid properties, classifying reservoir fluids into three categories: oil layers, oil–water coexisting layers, and water layers. The model was trained using a reliable sample library based on sensitive parameters, achieving a prediction accuracy rate of 90% in the validation set. The SVM algorithm demonstrates low computational complexity and strong generalization capabilities, proving to be highly effective in predicting fluid properties in complex reservoirs. This approach offers a new technical pathway for predicting fluid properties in the volcanic reservoirs of the Nanpu Sag and promotes the extensive application of machine learning techniques in the field of oil and gas exploration.