Smart infrastructure platforms increasingly join phasor measurement streams, city-scale sensors, cloud data pipelines, cross-cloud machine-learning workloads, hybrid evidence retrieval, long-horizon forecasting, API gateway governance, semi-supervised confidence gates, and GPU-accelerated optimization. The resulting systems must make fast operational predictions while preserving data scope, policy eligibility, and audit evidence. This survey examines smart infrastructure, forecasting, and accelerated optimization as one control problem. We synthesize recent work on intelligent PMU processing, governed cloud data pipelines, cross-cloud ML optimization, hybrid semantic-relational retrieval, historical forecasting, multi-cluster API gateway governance, reviewer and legal profile modeling, agreement-gated learning, and parallel stochastic optimization. The survey identifies five recurring design functions: stream evidence formation, chronology-aware forecasting, governed placement, confidence-gated action selection, and accelerated model refresh. A comparative coding of representative systems shows that infrastructure intelligence is strongest when forecasting and optimization are mediated by evidence packs, policy checks, and rollback-aware control loops. The paper concludes with design guidance for 2025 deployments that need low-latency analytics without sacrificing governance.
Sekar et al. (Sat,) studied this question.