AI-Enabled Cloud-IoT Platform for Predictive Infrastructure Automation

Modern infrastructure systems such as smart buildings, transportation networks, energy grids, and industrial facilities increasingly rely on interconnected sensing and control technologies. However, traditional infrastructure management approaches remain largely reactive, leading to inefficiencies, unexpected failures, and high operational costs. This paper proposes an AI-enabled Cloud IoT platform designed to support predictive infrastructure automation through real-time data acquisition, intelligent analytics, and automated decision making. The proposed framework integrates distributed IoT sensors with scalable cloud computing and machine learning models to enable predictive maintenance, fault detection, and adaptive control. By leveraging artificial intelligence techniques such as time-series forecasting, anomaly detection, and reinforcement learning, the system anticipates infrastructure degradation and optimizes operational responses. The architecture emphasizes scalability, interoperability, cybersecurity, and low-latency communication. Experimental analysis and comparative evaluation demonstrate that the proposed platform significantly improves system reliability, reduces downtime, and enhances automation efficiency compared to conventional rule based infrastructure systems. The findings confirm that AI-driven Cloud-IoT integration is a critical enabler for next-generation intelligent infrastructure management.