The integration of Deep Learning (DL) and Evolutionary Algorithms (EAs) has gained increasing attention as a strategy for addressing complex problems in optimization, learning, and decision-making. While DL offers powerful data-driven representation learning, it often struggles with hyperparameter sensitivity and generalization in dynamic environments. EAs provide robust global search capabilities but can be computationally intensive and inefficient in high-dimensional spaces. This review examines how combining these complementary approaches can overcome their individual limitations. It introduces a structured taxonomy of hybrid DL–EA systems, categorizing methods based on their functional roles and interaction patterns. Key application areas including multi-objective optimization, resource management, and adaptive control are analyzed to illustrate practical impacts. The paper identifies common challenges and outlines future research directions, aiming to support the development of effective and adaptive hybrid systems.