The strong heterogeneity of volcanic gas reservoirs and the multiple factors affecting the open flow potential of gas wells make it difficult for traditional methods of predicting the open flow potential of gas wells to balance computational efficiency and accuracy. In response to the above issues, this study introduces the data-driven Extreme Gradient Boosting algorithm (XGBoost) and proposes an algorithm that integrates the gas permeation mechanism and the data-driven approach to construct a single-well open-flow potential model for volcanic gas reservoirs based on the physically guided XGBoost algorithm (PG-XGBoost). This study is based on actual data from 50 gas wells in the Dixi block of the Kelameili gas field. Through the dual screening of the Mean Decrease Impurity (MDI) algorithm and Spearman correlation coefficient analysis, a comprehensive quantitative analysis is conducted on seven factors affecting the open flow potential of gas wells, including reservoir lithology, permeability, porosity, formation pressure, reservoir thickness, degree of fracture development, and fracturing treatment. The key factor for the open flow potential of gas wells is selected. Based on this, the XGBoost algorithm is used to construct a prediction model for the open flow potential of gas wells, and the binomial gas well productivity equation is used as the characterization formula for the gas seepage mechanism. Combining with the loss function of the XGBoost algorithm, a physics-guided XGBoost algorithm is constructed. Furthermore, the actual data of gas wells in the Dixi block are applied for blind well testing to evaluate the accuracy of the PG-XGBoost algorithm in predicting the open flow rate of gas wells. The results indicate that permeability, formation pressure, fracturing, reservoir lithology, and the degree of fracture development are the key factors for the open flow rate of a single well in the volcanic gas reservoir of the Dixi block in the Kelameili gas field. The PG-XGBoost algorithm was tested on 5 gas wells in this block, and the prediction accuracy of the open flow rate is 88.0%, which is 15.2% higher than that of the data-driven XGBoost algorithm. Therefore, using the binomial productivity equation as a physical constraint for the data-driven algorithm can effectively characterize non-Darcy gas flow and improve the prediction accuracy of the XGBoost algorithm for the gas well open flow rate. The method in this study can accurately predict the open flow potential of a single well in volcanic gas reservoirs, providing a technical path for predicting the open flow potential of complex gas reservoirs such as volcanic gas reservoirs.