Embodied intelligence extends artificial intelligence from information processing in the digital domain to autonomous interaction with the physical world. Despite rapid progress, the field still faces challenges such as ambiguous conceptual boundaries, fragmented technical frameworks, and insufficient alignment between application requirements and evaluation standards. This review presents a systematic framework for understanding embodied intelligence from five perspectives: conceptual foundations, key technologies, representative applications, evaluation methodologies, and future challenges. We first distinguish embodied intelligence from teleoperation and conventional automation, and establish an evolutionary framework spanning disembodied intelligence, spatial intelligence, physical intelligence, and embodied intelligence. Then we summarize key technologies in embodiment, the cerebellum, brain architectures, data acquisition, and training approaches, with particular emphasis on the evolution from traditional hierarchical modular architecture to large-model-driven modular architecture, Vision-Language-Action (VLA) model architecture, world model architecture, and world action model architecture. Representative applications are categorized into structured-environment manipulation, unstructured-environment interaction, and high-risk open-domain exploration. To bridge technological development and real-world deployment, we further review three evaluation paradigms which include data-based evaluation, simulation-based testing and real-world testing. Finally, we identify eight major challenges which cover data quality, long-horizon planning, multimodal fusion, edge deployment, safety and trustworthiness, continual learning, unified navigation and manipulation, and multi-agent collaboration. This review provides a systematic perspective on embodied intelligence and highlights key directions for advancing embodied systems toward reliable deployment in complex real-world environments.