Urban gas pipelines must contend with situations such as road construction and excavation for house building, where short-term emergencies leading to large-scale leaks pose significant risks to both people and the environment. To enhance the response cycle for detecting leaks in urban natural gas pipelines, this paper proposes a real-time flow prediction model for gas pipelines. This model is an improved version of the Long Short-Term Memory (LSTM) neural network, utilizing an ensemble learning algorithm. It processes the instant flow data from preprocessed historical flow meters as input and fine-tunes the neural network’s hyperparameters through grid search. The LSTM, with its inherent temporal memory function, serves as a weak predictor within the ensemble, which is then strengthened through a weighted combination using the Adaboost ensemble learning algorithm. The findings indicate that our approach, in comparison to a singular LSTM network, yields lower Mean Squared Error (MSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE). The enhanced LSTM model with ensemble learning significantly improves time-series forecasting accuracy, exhibiting robust generalization and stable predictive performance, thus providing critical insights for real-time monitoring and intelligent alarm systems in urban gas networks.