<![CDATA[Modern urban planning faces the challenge of achieving optimal density without sacrificing environmental quality and sustainability. The conventional linear, trial-and-error design process is considered ineffective in responding to these needs. This study aims to compare the effectiveness of AI Prediction in Autodesk Forma with conventional design methods in spatial planning decision-making, using a Performance-Based Design approach in an urban area case study. Various performance criteria, including density, sun exposure, microclimate comfort, noise, and embodied carbon, were included as constraints and targets, then explored through thousands of design iterations by the AI algorithm. The results show that Autodesk Forma significantly speeds up the design exploration process from weeks to hours or days, while producing solutions with more balanced multi-criteria performance. The platform is able to achieve density targets while reducing embodied carbon and improving the quality of public spaces. The study concludes that Autodesk Forma plays an important role in the transformation of AI-based urban design, producing faster, more adaptive, sustainable, and resilient solutions, and encouraging the adoption of generative technology as a new standard practice in urban planning.]]>
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