Enhancing the precision of fuel monitoring through the utilization of convolutional neural network (CNN) algorithm compared to regression algorithm is the primary objective of this investigation. The primary data source for this fuel monitoring is the data set. Group I and Group II, each with twenty samples, were the two groups used for the analysis. Group I employed convolutional neural networks (CNNs), while Group II employed linear regression. In total, forty samples were present. Mainly comparing CNN and linear regression approaches’ performance, the inquiry used accuracy score as the primary assessment metric. The CNN algorithm demonstrated a higher accuracy of 94.5%, outperforming the regression algorithm, which achieved an accuracy of 81.2%, indicating statistical significance. In conclusion, the precision of fuel monitoring is significantly enhanced through the utilization of the CNN algorithm compared to the regression algorithm.