This survey paper provides a comprehensive analysis of deep learning advancements in humanoid robot vision, focusing on key areas such as real-time object detection, 3D scene understanding, and multimodal sensor fusion. We systematically review foundational architectures (CNNs, Transformers) and emerging paradigms (self-supervised learning, embodied AI) that enable robust visual perception in dynamic environments. The paper highlights critical algorithmic innovations, including YOLO variants for efficient detection, spatial-aware transformers for 3D reasoning, and fusion techniques for heterogeneous sensor integration. We identify persistent challenges in computational efficiency, generalization under environmental dynamics, and ethical deployment considerations. The survey synthesizes interdisciplinary research directions, emphasizing neuro-symbolic integration, open-vocabulary perception, and energy-efficient architectures as pivotal for advancing autonomous human-robot interaction. By evaluating 60+ works, this study establishes a roadmap for overcoming current limitations while leveraging breakthroughs in visual representation learning and embodied cognition to push the boundaries of robotic vision systems.