This study presents a kinetic and energy-informatics analysis of biomass pyrolysis, integrating experimental data with genetic-algorithm-optimized Arrhenius modeling to elucidate feedstock-specific conversion behavior. Three representative biomass types, nut shells and eucalyptus, were investigated to quantify how compositional differences influence reaction kinetics, product distribution, and energy recovery. Experimental data were fitted using a parallel–consecutive three-lump kinetic scheme describing primary and secondary devolatilization into gas, tar, and char. The GA optimization achieved excellent agreement with experimental results (R2 > 0.95), yielding activation energies and pre-exponential factors consistent with the structural complexity of lignocellulosic components. Results reveal that nut shells, rich in lignin and aromatic polymers, exhibit slower decomposition dominated by stable char formation, while cellulose-rich eucalyptus undergoes rapid devolatilization, favoring gas and tar evolution. Energy yield analysis, based on lower heating values, indicates total recoverable energies of 19–25 MJ kg⁻1 biomass, corresponding to 5–7 kWh kg⁻1. Sensitivity analysis demonstrated that gas yields are highly responsive to kinetic parameter perturbations, whereas char yields remain relatively stable, confirming the robustness of char-oriented systems under kinetic uncertainty. Beyond traditional kinetic interpretation, this work establishes a methodological bridge between reaction kinetics and system-level energy metrics. By embedding GA-optimized kinetic parameters within energy-informatics frameworks, the study enables predictive modeling of feedstock behavior, process optimization, and integration of pyrolysis within distributed, carbon-neutral bioenergy networks. The findings contribute to the digitalization of biomass-to-energy conversion and provide a transferable foundation for the development of data-driven, sustainable bioenergy systems.