In practically every engineering application, computational fluid dynamics (CFD) is useful for creating constructed environments that improve comfort, health, energy efficiency, and safety. However, most CFD models still take too long to compute for the majority of engineering applications. Recent developments in artificial intelligence (AI) and machine learning (ML) offer a fantastic chance to create quick data-driven models for fluid flow and other physics-related phenomena. The effective integration of ML with CFD techniques is a growing area of research for scientific machine learning, or SciML. The main goal of this study is to compile and discuss current and upcoming fluid dynamics trends pertaining to AI and ML-based technologies used to solve fluid flow challenges. Several novel AI-based models will be presented with their benefits and drawbacks, with which an informed selection of models can be made in future research and engineering applications. Research showed that AI-based techniques performed better when compared to conventional methods by 6.6%, 11.1%, and 12.75% for root mean squared error, mean absolute error (MAE), and coefficient of mean square error evaluation metrics, respectively. However, novel models like physics-informed neural networks provide a hybrid between data-driven and physics-driven models, requiring further research into their efficiency for fluid dynamics. This chapter’s application represents current AI uses in fluid mechanics and multiphase flow systems to appropriately select AI-based models in future works and research. Limitations on sparse datasets, noise, and levels of integration of AI-based algorithms require further research on AI in fluid mechanics.