Feature selection (FS) remains essential for reducing dimensionality, improving model accuracy, and ensuring efficient learning in the age of big data. Hybrid evolutionary algorithms—carefully combining two or more metaheuristics—have recently emerged as powerful search mechanisms for identifying optimal feature subsets while balancing global exploration and local exploitation. This keynote paper presents a forward-looking perspective on the design and deployment of hybrid evolutionary feature selection. Building on our recent research and related high-impact studies, we distill the conceptual foundations of hybridization, highlight emblematic algorithmic synergies such as genetic–particle swarm and grey-wolf–hawk optimizers, and demonstrate their impact in diverse application domains, from medical imaging to text analytics. Beyond summarizing proven methods, we identify key challenges—scalability to ultra-high-dimensional and streaming data, integration with explainable AI, and energy-aware optimization—that must be addressed to meet the demands of next-generation intelligent systems. The paper concludes with a call for cross-disciplinary collaboration and industrial uptake of hybrid evolutionary feature selection.