Reliable estimation of biochar’s calorific value is essential for optimizing its use as a renewable energy source. Traditional bomb calorimetry provides accurate measurements but is hindered by its destructive, time-consuming nature, limiting the high-throughput screening capabilities needed for large-scale deployment. In this study, an innovative, non-destructive approach utilizing laser-induced breakdown spectroscopy (LIBS) combined with advanced multivariate analysis is presented for predicting the Higher Heating Value (HHV) of biochar derived from pine and beech biomass. The developed empirical model incorporates spectral signatures of key elements: carbon, hydrogen, nitrogen, sulfur, and oxygen. Model validation using 36 independent biochar samples revealed a statistically significant correlation between experimentally measured and LIBS-predicted HHVs (p-value = 0.045, t-statistic = 2.08). The developed model yielded a mean absolute error (MAE) of 1.33 MJ kg⁻¹ and a root mean square error (RMSE) of 1.72 MJ kg⁻¹. The findings demonstrate the feasibility of using LIBS-derived elemental data for rapid HHV estimation and provide a basis for further model refinement through the inclusion of additional biochar types and calibration datasets. The model effectively captures the complex nonlinear relationships between spectral features and energy content, addressing the heterogeneity inherent in biochar matrices. These findings highlight LIBS’s potential as a rapid, scalable, and environmentally sustainable tool for real-time biochar evaluation. Implementing this approach could significantly accelerate biomass resource assessment, optimize bioenergy production, and advance sustainable energy management strategies aligned with global environmental goals.