This study presents an in-depth analysis of gasoline price forecasting using the Adaptive Network-Based Fuzzy Inference System (ANFIS), leveraging a comprehensive dataset from the U.S. Energy Information Administration spanning 30 years (1993-2023). The ANFIS model combines the strengths of fuzzy logic and neural networks to capture complex, nonlinear relationships in the data, enhancing the accuracy of price predictions. The dataset is preprocessed by decomposing dates into year, month, and day components. The methodology involves systematic data preparation, model training with previous week's prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. Results show that incorporating previous prices significantly improves the model's accuracy, achieving a score of 0.9970 and a correlation of 0.9985. The findings have significant implications for energy sector stakeholders, enabling informed decision-making, risk management, and strategic planning. The study also compares the ANFIS model with traditional time series models like VAR and ARIMA, demonstrating the ANFIS model's superior performance.This study presents an in-depth analysis of gasoline price forecasting using the Adaptive Network-Based Fuzzy Inference System (ANFIS), leveraging a comprehensive dataset from the U.S. Energy Information Administration spanning 30 years (1993-2023). The ANFIS model combines the strengths of fuzzy logic and neural networks to capture complex, nonlinear relationships in the data, enhancing the accuracy of price predictions. The dataset is preprocessed by decomposing dates into year, month, and day components. The methodology involves systematic data preparation, model training with previous week's prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. Results show that incorporating previous prices significantly improves the model's accuracy, achieving a score of 0.9970 and a correlation of 0.9985. The findings have significant implications for energy sector stakeholders, enabling informed decision-making, risk management, and strategic planning. The study also compares the ANFIS model with traditional time series models like VAR and ARIMA, demonstrating the ANFIS model's superior performance.