This study investigated a precise algorithm combining X-ray Fluorescence and Neural Network (XRF-NN) for predicting ash content. The 261 sets of XRF tests show that the 34 elements in the chosen Guqiao coal can be categorized as major, secondary, and tiny elements, whose cumulative ratios were ~95%, 4-5%, and <1%, respectively. Referring to the machine learning theory, the construction strategy of the Element-Ash dataset was determined viz. value determination → standardization → division → optimization of generalization ability. Then, the hyperparameters optimization displays that the XRF-NN model with 34 inputs and 1 output was suitable to predict coal ash content, where the activation function, loss function, and optimizer were ReLU, MAE, and Momentum, respectively. After iterative training, the new XRF-NN model provides the precise prediction of coal ash content with absolute errors between -2.0% and 2%. Moreover, the prediction accuracy rose from 57.69% to 100%, as the expected relative error increased from 1% to 5%. Furthermore, the comparisons between different prediction methods reveal that the minimum MRE of 1.22% can be obtained by XRF-NN with total elements, which was only half of those given by the conventional Multiple Linear Regression and Partial Least Squares Regression. Besides, the XRF-NN model presents the root mean squared error of 0.797%, a mean absolute error of 0.625%, and the coefficient of determination of 0.999, which were significantly superior to those calculated by Dual-Energy Gamma-ray Transmission, Ploy, RFR, XGBoost, and DNN model. The results of this study suggest the excellent performance of the new XRF-NN model in predicting ash content.