World models are a transformative paradigm in embodied AI, enabling agents to learn efficiently and plan by simulating environmental dynamics. As vision-language action and navigation systems grow more sophisticated, integrating world models is crucial for bridging multimodal perception, language understanding, and sequential decision-making. However, existing surveys, which focus on application domains or learning paradigms, have overlooked a critical architectural question: how world models should be structurally integrated into these systems. To address this gap, we introduce an integration-centric taxonomy that classifies research into three fundamental architectural paradigms: (1) Modular Architectures, where world models and policies are distinct modules; (2) Sequential Architectures, implementing hierarchical plan-then-execute workflows; and (3) Unified Architectures, which fuse world prediction and action generation into an end-to-end network. Through this framework, we systematically analyze the inherent trade-offs across these paradigms: modular designs excel in interpretability, sequential approaches enable hierarchical reasoning, and unified models achieve tight prediction-control coordination. Based on this analysis, we outline promising research directions and provide architectural principles for effectively integrating world models, ultimately to advance more efficient, interpretable, and generalizable agents.