Advance Infratructure Maintenance Using Parallel Data Processing with Visual Analytics and IoT

A very important strategy for strengthening the security, durability, and efficiency of civil infrastructure is predictive maintenance. This research explores how to assess critical assets such as highways, bridges, and dams by integrating sensor networks, machine learning algorithms, and structural health monitoring techniques. Apache Kafka's realtime data streaming allows for efficient handling of large data volumes, and a Flutter-Dart interface provides easy access to predictive insights. Using predictive analytics enables stakeholders to predict their need for maintenance, improves their schedules, and lowers the probability of costly breakdowns. This study underscores the potential for predictive maintenance in making infrastructure management resilient against downtime and maximizing returns.