The integration of robotic technologies into existing industrial infrastructure networks has become a critical requirement for modern manufacturing and production ecosystems. However, heterogeneous legacy systems, limited interoperability, and scalability constraints continue to hinder seamless deployment. This paper presents a comprehensive survey-driven architectural framework that addresses system-level challenges in integrating robotic systems into established industrial environments. The study synthesizes recent advancements in cloud robotics, decentralized architectures, intelligent control systems, and modular manufacturing reconfiguration strategies. Particular emphasis is placed on evaluating scalable orchestration models and adaptive control mechanisms that support heterogeneous robotic platforms. Existing literature indicates that efficient integration requires not only hardware-software compatibility but also dynamic system coordination and optimized resource allocation strategies (Dawarka & Bekaroo, 2022). Furthermore, simulation-based approaches for determining optimal robotic system density highlight the importance of structural planning in modular production environments (Marschall et al., 2022). The proposed architectural model consolidates these insights into a layered system design incorporating perception, control, coordination, and cloud integration layers. The study also identifies key limitations in current frameworks, including latency issues, interoperability gaps, and insufficient real-time adaptability. The findings contribute toward establishing a scalable and standardized integration pathway for industrial robotics deployment in complex operational environments.