Purpose: This paper presents an accurate estimation and forecasting of gasoline consumption. This is vital for the policy and decision-making process in the energy sector. Method: A hybrid data-driven model based on Artificial Neural Network (ANN) and an autoregressive integrated moving average (ARIMA) approach was developed for optimum estimation and forecasting of gasoline consumption. The proposed hybrid ARIMA-ANN approach considers six lagged variables and one forecasted value provided by the ARIMA process. The ANN trains and tests data with a multi-layer perceptron (MLP) approach, which has the lowest Mean Absolute Percentage Error (MAPE). To show the applicability and superiority of the proposed hybrid approach, daily available data were collected for 7 years (2015–2021) in Iran. Results: The acquired results show a high accuracy of about 94.27%  using the proposed hybrid ARIMA-ANN method. The results of the proposed model are compared with respect to the regression models and the ARIMA process. Conclusions: Analyzing consumption patterns can provide insights into consumer behavior, enabling NIORDC to tailor its services and marketing strategies more effectively. Limitations: Eliminating subsidies from gasoline prices has led to the appearance of noisy data in gasoline consumption in Iran's National Oil Refining and Distribution Company.   Contribution: The outcome of this paper justifies the capability of the proposed hybrid ARIMA-ANN approach in accurate forecasting of gasoline consumption.