In the past few decades, several research attempts have strived to utilize artificial intelligence (AI) in solving computational fluid dynamics (CFDs)-related problems; translating into AI/CFD systems. This increasing trend has been motivated by the documentation that AI/CFD systems are better placed and promising relative to their successful application to some of the well-formulated CFD problems that require pre-enumerated solution selection or classification. However, some scholarly observations contend that when CFD tasks are formalized or understood poorly, the application of AI technology leads to a large investment of effort and long system development times, with payoff unguaranteed. Given this dilemma, it becomes important to examine some of the AI/CFD or AI-based CFD approaches that have been applied to different CFD tasks, as well as some of the factors affecting the success of the perceived approaches. In CFD practice and research, all phases constitute two broad categories of activities. On one hand, some CFD activities require human perception, reasoning, and knowledge. On the other hand, some CFD activities require high-speed numerical processing by computers. Imperative to highlight is that efforts aimed at enhancing the CFD methodology have examined both forms of activities. Particularly, CFD problem decision making by humans (regarding solution interpretation and problem setup) has been enhanced through the use of high-speed color graphics, while the computational part has been streamlined using improved algorithms, solutions methods, and grid generators. Despite these efforts, there remains a traditional labor separation between computers and humans, whereby computers play the role of number computation while humans participate in decision making. During the establishment of CFD solutions, most of the reasoning used during intermediate solution assessment, graphical display, code execution, data format, parameter adjustment, discretization, and geometry definition has proved to be a rate-limiting step, that requires some expertise or experience, and is sensitive or prone to error. These limitations have led to increasing research on AI with the aim of ensuring that some of the CFD-related tasks that humans perform currently are automated. Some of the merits associated with the perceived automation of CFD tasks include reductions in the CFD solution turnaround time, consistency in application, the distribution, preservation, and codification of expertise, and relief from tedium. This study has examined some of the AI-based CFD approaches that have gained applications relative to the setup and solution of CFD problems. Some of the specific AI techniques that have gained increasing application in CFD tasks include the use of the Elman neural network (ElmanNN) as an AI in CFD�s hull form optimization, the use of genetic algorithm (GA)-based CFD multiobjective optimization, the implementation of fluid flow optimization using AI-based convolutional neural networks (CNNs) CFD, the use of coupled AI (via ANN) and CFD in predicting heat exchanger thermal-hydraulic performance, and the use of artificial neural networks (ANNs) in CFD expert systems. Overall, the majority of these AI-based integrations improve the performance of CFD solvers, especially regarding system optimization. © 2020 Elsevier Inc.