Renewable energy technologies and resources have witnessed a notable upsurge in research interest recently. Because of the complexity and ambiguity inherent in real-models, selecting optimal renewable energy technologies may be a laborious process for decision-makers. To address these issues, this study introduces a modified Multi-criteria Group Decision-Making (MCGDM) fuzzy optimization model to directly obtain the crisp weights from the fuzzy decision matrices. The model is solved using an algorithm created in a metaheuristic environment, employing a hybrid Particle Swarm Optimization (PSO) technique. This model is implemented in a real-world problem of selection of a suitable renewable energy source. The results indicate that wind energy emerges as the best non-conventional source within the given constraints. Comparison results confirm that the algorithm provides a similar ranking when selecting renewable energy technologies. By integrating the optimization process, the model enables more accurate and reliable decision-making results in the sector of renewable energy technologies and resources.