Natural gas forecasting accuracy is crucial in the management of energy resources, economic planning, and environmental sustainability. The current work presents a hybrid model combining RNNs and BP to improve accuracy in the forecasting of natural gas time series. The hybrid model incorporates the strong side of RNNs for capturing time dependencies into the gradient adjustment used by BP in optimizing its model parameters. The suggested model resolves the problem of traditional ones that cannot deal with the nonlinearity of natural gas data. The model was evaluated with standard error measures: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The RMSE was about 6.22, and the MAPE was about 21.1 percent; hence, the model may represent time series well while staying stable during training. Validation checks and gradient values were used in this model to show that it may generalize unseen data without excessive overfitting. This paper extends time series forecasting through a strong hybrid model in the fusion of the temporal modeling capacity of RNNs with the optimization power of BP. The outcome is that the hybrid model could be a promising solution to natural gas forecasting; applications in energy management and decision-making can be further elaborated. Future works may study advanced architecture like LSTM or GRU or incorporate attention mechanisms in other methods to improve predictions and performance of the model.