Artificial Intelligence (AI)-driven controllers have increased the accuracy, flexibility, and efficiency in flow rate control for gas and liquid flows. In contrast, traditional Proportional–Integral–Derivative (PID) controllers are usually not well trained for nonlinear dynamics and time-varying disturbances, leading to limited stability and control accuracy. Most existing research studies have focused on the design and performance of individual AI-based controllers for individual machines without systematically comparing these devices for various industrial and energy applications. The gap of this study is filled by a review of 34 peer-reviewed studies from 2021 to 2024 detected in Scopus and Web of Science databases, classified on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) structure. From the quantitative results the AI controllers have a higher performance overall than traditional PID controllers with faster response by 12 to 85%, 15 to 67% reduction in steady-state errors, and 18 to 40% drop in overshoot, which indicates high accuracy and stability of systems. The data indicates that fuzzy and hybrid controllers are highly flexible for dealing with nonlinear and dynamic flow phenomena while model predictive and optimization-based controllers have high accuracy for multivariable processes. Furthermore, AI control technology for energy, hydrogen, and process fluid applications improves operability, reduces energy overhead, and enables real-time flexible management under ever-changing loads. Thus, this review lays the excellent groundwork for next-generation intelligent control frameworks and opens the new paradigm of advanced and data-driven strategies towards subsequent flow regulation and energy system applications.