The growing capabilities of deep learning and artificial intelligence (AI) have revolutionized numerous fields, including indoor airflow simulations. Computational Fluid Dynamics (CFD) plays a crucial role in simulating airflow, heat transfer, and pollutant dispersion, which are essential for healthier indoor climates. However, the progress in this domain is hampered by the lack of publicly available CFD datasets that are consistent and standardized. With the urgent need to solve the recent COVID-19 crisis there has been a new wave of published studies to inspect airflow patterns in confined spaces. By reviewing many recent CFD applications in indoor environment, this study shows a perspective on the critical importance of establishing a robust, open-access repository of CFD data for indoor airflow simulations. Such a resource would not only accelerate research by providing a rich dataset for training AI models but also ensure that findings are replicable and comparable across different studies. This paper advocates for larger efforts to create accessible and standardized CFD datasets, which will be instrumental in leveraging AI for the development of smarter, healthier, and more sustainable cities.