Science and technology are advancing rapidly, while environmental conditions, climate, and human needs constantly change. As a result, practical knowledge from historical records, books, and scientific documents is often incomplete. Expert experience helps fill these gaps, but it is usually unstructured and based on empirical rules. This study develops a model that systematically integrates practical knowledge with empirical rules into a structured and user-friendly computational framework. The Taguchi orthogonal array method is applied to reduce the number of study cases while preserving essential information, overcoming the factorial method’s limitations. This also facilitates structured questionnaire design for expert input. Additionally, regression analysis is used to develop predictive equations for architectural design variables based on practical and empirical data. By combining these methods, the model effectively bridges the gap between theoretical knowledge and expert intuition. Applied to green space planning in architectural design, the model provides a systematic approach to incorporating expert knowledge in cases without standardized guidelines. This enhances decision-making, ensuring more adaptable and effective solutions for tree planning in urban and architectural design.