Historical Small Towns (HSTs) face a challenge: preserving their charm and cultural heritage while fostering economic growth. The Circular Economy (CE) offers a promising solution. This study introduces the H-SMA-CE project, which utilizes a Decision Support System (DSS) to optimize waste management and energy efficiency in HSTs, all while safeguarding their unique character. The DSS employs a multifaceted approach. Users define scenarios tailored to their specific HST context, focusing on goals like minimizing waste or maximizing energy efficiency. The system integrates data on demographics, economics, and environment from reliable sources. Finally, a spreadsheet-based model analyzes various CE interventions (e.g., composting programs, recycling facilities). It prioritizes interventions based on feasibility, economic viability, and environmental impact. Preliminary findings suggest the DSS empowers decision-makers with valuable data for informed CE investments. Further, the open-source nature of the tool facilitates knowledge sharing and replication of successful CE models across HSTs. Future research will focus on seamless integration with existing data systems within HSTs, accounting for dynamic changes (population, economy, infrastructure), and advocating for policies that support CE adoption. This research contributes to achieving sustainable and circular development in HSTs. By providing a novel decision-making tool, the H-SMA-CE project paves the way for a more sustainable future for these historically significant places.