Efficient management of ligneous post-harvest fruit crop waste is key for sustainable energy use. This type of waste, unlike typical agricultural waste, is extremely difficult to manage by digestion, thus green methods of direct conversion to energy are being explored. This study investigates, and models using Machine Learning methods, the biomass energy potential and emission characteristics of annual shoots from six types of primocane raspberry cultivars of Rubusidaeus L. Significant cultivar-based variations in calorific properties were observed, (Higher Heating Value: 17.76–17.18 MJ kg−1, Lower Heating Value: 16.52–15.93 MJ kg−1). An artificial neural network (ANN) model, combined with a genetic algorithm, optimized biomass selection for bioenergy production. The ANN demonstrated high accuracy, with average relative error below 2 % across datasets. According to model, key agronomic traits—shoot thickness and branch density—maximized energy efficiency and minimized emissions. This study provides a novel framework for bioenergy optimization from raspberry residues, demonstrating the potential of advanced computational methods to enhance renewable energy production in a clean and efficient way. These findings offer insights into the sustainable use of agricultural residues for energy generation and emphasize the potential of machine learning.