<![CDATA[The advancement of computational technology in architecture has led to the emergence of parametric design methods that enable a more adaptive, efficient, and measurable design process. Within the framework of sustainable architecture, such approaches hold significant potential to optimize thermal comfort, natural lighting, and building energy efficiency—particularly in tropical climates. This study aims to develop a Python-based parametric design framework that functions as a decision-support tool for sustainable architectural design. The research method consists of a literature review, the development of parametric scripts in Python integrated with design software such as Rhinoceros–Grasshopper, and performance simulations based on actual climatic data. Evaluation was carried out by analyzing the simulation outcomes across several design variables, including building orientation, roof geometry, and window configuration. The results indicate that the proposed framework provides flexible design responses according to environmental parameters and achieves an energy efficiency improvement of up to 25% compared with conventional design methods. Moreover, the system facilitates the design iteration process and assists architects in making data-driven design decisions. In conclusion, the Python-based parametric approach presented in this study offers an effective solution to advance sustainable architectural practice in the future.]]>
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