This study explores decision-making support systems (DMSS) for optimal renewable energy pricing, focusing on a comparative analysis between Artificial Intelligence (AI) tools and System Dynamics (SD) models. AI tools, known for their adaptability and handling of complex datasets, are contrasted with SD models, valued for their holistic view and feedback loop capabilities. Through a detailed comparative framework, we assess accuracy, adaptability, complexity, transparency, and implementation cost. Our findings highlight the strengths and weaknesses of each approach, demonstrating context-specific advantages. Additionally, we propose a hybrid model that integrates AI and SD, leveraging the strengths of both methodologies to enhance decision-making in renewable energy pricing. This hybrid model aims to provide a more robust and comprehensive tool for policymakers and stakeholders. The study concludes with recommendations for future research and practical applications in the renewable energy sector.