As biomass becomes increasingly important as an energy source, predicting its higher heating value using more efficient algorithms based on schedule information, such as imminent analysis, enables timely decisions on bioenergy usage. A random search optimiser is used to describe higher heating values of raw biomass using regression analysis methodologies based on experimentally discovered properties. A unique artificial smart model based on an intriguing approach was developed as well as an artificial neural network with a random search optimiser for determining the higher heating value of raw biomass. The current study and findings include the importance of each physicochemical parameter on raw biomass higher heating value predictions using this unique prototype. According to the new model used in this scenario, the estimated coefficients of determination and correlation coefficients resulting from the data analysis are 0.7554 and 0.9999, respectively, when the new model is applied to the analysis of the data. The importance of employing learning machines may be seen in the appraisal of energy resources for energy systems to develop a bioenergy-specific algorithm. The unanimity between the recorded data and the regression archetype or artificial neural network on which it was built showed a high degree of efficiency. There was a strong correlation between the random search/artificial neural network-based method and the random search/regression-based method regarding how well the model fit observed data, indicating that the second method exhibited the best results.