This study presents the prediction of the higher heating value (HHV) of microalgal biomass using artificial neural networks (ANN) as an alternative to traditional experimental methods, which require specialized infrastructure and high operational costs. In light of the increasing global energy demand and environmental issues caused by fossil fuels, the energy characterization of microalgal biomass for the production of third-generation biofuels emerges as a sustainable option. The objective was to develop an artificial intelligence model based on ANN to predict the HHV of microalgal biomass from proximate and elemental analysis data. Three microalgal species (Chlorella sp., Haematococcus sp., and Arthrospira sp.) were cultured under controlled conditions, harvested, pretreated, freeze dried and energetically characterized. HHV was determined by calorimetry and estimated using an ANN trained in Matlab with 400 neurons and a Bayesian regularization algorithm, integrated into a graphical interface. Haematococcus sp. showed the highest HHV (25.87 MJ/kg), followed by Chlorella sp. (22.11 MJ/kg) and Arthrospira sp. (20.00 MJ/kg). The ANN model achieved high predictive accuracy (R = 0.969, MSE = 6.58E-03) and an average prediction error of 0.63%, significantly outperforming traditional empirical equations. The ANN-based model significantly improved HHV estimation, enabling rapid and cost-effective energy assessment of microalgal biomass. The findings demonstrate the model’s potential as a practical tool for supporting sustainable biofuel production and highlight future work aimed at expanding the dataset, integrating hybrid models and real-time data, and validating the tool at pilot and industrial scales.