This colloquium centers on recent advances in active flow control, highlighting its impact across sectors such as energy, industry, and transportation. Emphasis is placed on emerging data-driven approaches, including reduced-order models enhanced by neural networks and reinforcement learning, which enable effective control of complex fluid systems. By integrating physics-based modeling with modern computational tools, these methods offer new avenues for designing efficient open- and closed-loop controllers. The event aims to promote knowledge exchange on cutting-edge strategies for flow control and foster collaboration among European and international experts in data-driven fluid mechanics.