Abstract : The growing deployment of industrial robotics and artificial intelligence–driven automation across logistics, manufacturing, and large-scale industrial environments has fundamentally altered the requirements placed on the underlying compute and network infrastructure that supports these systems. Traditional on-premises deployments, while providing acceptable proximity to field devices, fail to deliver the elastic scalability and operational automation that modern robotics platforms demand; at the same time, purely centralized cloud architectures introduce latency characteristics that are structurally incompatible with the deterministic timing requirements of real-time control loops. This review examines the architectural principles governing multitier cloud network infrastructures designed to reconcile these competing demands, with particular focus on metro-proximate Local Zone deployments, hyperscale backbone connectivity between infrastructure tiers, failure domain isolation strategies, and deterministic network monitoring frameworks. The review further analyzes how workload separation across well-defined infrastructure tiers allows robotic command and control functions to operate with low-latency determinism while resource-intensive application services scale freely within centralized cloud regions. A simulation-based quantitative evaluation conducted across four architectural configurations, the proposed four-tier architecture, a region-centric cloud baseline, a generic three-tier edge-cloud baseline, and an on-premises reference, demonstrates, under simulation conditions representative of public-internet-routed region-centric deployments, a 17.6-fold reduction in P99 control loop latency, a 411-fold improvement in backbone failure recovery time, and a fleet-aggregate availability of 99.94% relative to 97.83% for the region-centric baseline. The architectural patterns discussed here offer a durable engineering foundation for organizations building production-grade robotics systems at scale