The Cognitive Industrial Twin (CIT) has emerged as an advanced evolution of the Digital Twin (DT) paradigm for Industry 4.0 environments. By embedding learning, reasoning, and decision-making capabilities, CITs extend conventional DTs from passive monitoring toward adaptive and intelligent representations of industrial systems. This survey systematically examines the current state of CIT research, clarifies its definition, and distinguishes it from traditional DTs and other intelligent twin frameworks. A generic five-layer reference architecture is presented, encompassing data sensing, information fusion, knowledge cognition, autonomous decision-making, and feedback optimization. The paper further reviews key enabling technologies—including multi-modal data fusion, reinforcement learning, knowledge graphs, causal inference, and edge intelligence—and discusses their roles in supporting cognitive and autonomous twin functionalities. In addition, industrial applications across smart manufacturing, prescriptive maintenance, autonomous logistics, and sustainable production are analyzed, revealing a paradigm shift from open-loop monitoring to closed-loop, cognition-driven autonomy. Finally, emerging research directions—such as brain-inspired computing, large language model (LLM) integration, and hybrid physical–cognitive modeling—are outlined, along with the key challenges that must be addressed to enable the broader industrial deployment of CITs.