Computational fluid dynamics (CFD) is a valuable tool in designing built environments, enhancing comfort, health, energy efficiency, and safety in both indoor and outdoor applications. Nevertheless, the time required for CFD computations still needs to be reduced for engineering studies. Recent advances in machine learning (ML) techniques offer a promising avenue for developing fast-running data-driven models for physics-related phenomena. As scientific machine learning (SciML) research increasingly focuses on efficiently coupling ML and CFD techniques, this literature review highlights the growing number of applications in the built environment field to accelerate CFD simulations. This work aims to identify emerging trends and challenges in incorporating ML techniques into built environment flow simulations to foster further advancements in this domain. The prevailing approaches are direct surrogate modeling and reduced-order models (ROMs). Both approaches increasingly rely on deep learning architectures based on neural networks. The reviewed studies reported computational time gains of several orders of magnitude in specific scenarios while maintaining reasonable accuracy. However, several challenges remain, such as improving models' generalizability and interpretability, enhancing methodology scalability, and reducing the computational cost of developing the models. Efforts are underway to address more complex cases with advanced SciML techniques. Notably, incorporating physics into the learning process and hybridizing CFD solvers with data-driven models merit further investigation. The exploration of these approaches represents a crucial step toward the deployment of reliable models that enable fast design for built environment engineering studies.