## 6 Conclusions  
This research demonstrates that LSTM neural networks provide a transformative approach to sanitary facility planning, surpassing traditional queueing models by 39.26% in predictive accuracy (MAPE). The model effectively narrows the discrepancy between expected and actual usage, reducing deviation from 12.07-19.05% under prescriptive codes to just 2.63-4.76%. By integrating data-driven insights into facility design, this study marks a shift toward performance-based approaches that offer greater precision, adaptability, and efficiency. A key contribution of this work is the introduction of an AI-driven framework for predicting sanitary fixture requirements, which captures complex usage patterns with unprecedented accuracy. The analysis identified total occupancy (0.842), time of day (0.731), and gender ratio (0.695) as the most influential factors shaping fixture demand, underscoring the advantages of a dynamic modeling approach over static, rule-based systems. Further, the model validated the potential for a 15% reduction in total fixtures while maintaining service quality, highlighting significant opportunities for cost optimization. The practical implications of these findings extend beyond model performance to real-world building operations. The results indicate that implementing AI-driven planning could yield substantial financial and spatial benefits, with estimated construction cost savings of SGD 280,000 per building and annual maintenance savings of SGD 42,000. Additionally, optimized fixture distribution could free up approximately 45 m\({}^{2}\) of usable space, improving overall facility efficiency while enhancing user satisfaction through data-driven fixture allocation. Nevertheless, issues around data accessibility, interpretability, and broad testing remain. The model's performance is inherently dependent on the quality and quantity of available training data, and its current validation is limited to office building contexts. While the framework demonstrates strong predictive capability, real-time adaptation remains an area for further development. Also, cultural and regional variations in facility usage require further study to ensure broader applicability. Future research should focus on expanding validation across diverse building types and cultural settings, integrating IoT-enabled real-time occupancy monitoring, and exploring transfer learning techniques to enhance model adaptability. The development of explainable AI components would be crucial in increasing stakeholder trust and regulatory acceptance. Extending the methodology beyond sanitary facilities to other building systems could further enhance the efficiency and sustainability of built environments. In conclusion, this study represents a significant advancement in evidence-based facility planning. By combining machine learning with domain expertise, it illustrates how AI can make the built environment more responsive, cost-effective, and user-centric. Beyond immediate financial and operational benefits, this research lays the groundwork for a more data-driven approach to building design, positioning AI as a key tool in shaping the future of urban infrastructure. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15052499/s1](https://www.mdpi.com/article/10.3390/app15052499/s1)]([https://www.mdpi.com/article/10.3390/app15052499/s1](https://www.mdpi.com/article/10.3390/app15052499/s1)). Conceptualization, S.T. and T.T.T. ; methodology, S.T. and T.T.T. ; software, S.T. ; validation, S.T. and T.T.T. ; formal analysis, S.T. and T.T.T. ; investigation, S.T. and T.T.T. ; resources, S.T. and T.T.T. ; data curation, S.T. and T.T.T. ; writing--original draft preparation, S.T. ; writing--review and editing, S.T. and T.T.T. ; visualization, S.T. and T.T.T. ; supervision, T.T.T. ; project administration, S.T. ; funding acquisition, S.T. and T.T.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.