Floods are the natural disasters that cause catastrophic destruction and devastation of natural life, agriculture, property and infrastructure every year. Flooding is influenced by various hydrological & meteorological factors. A number of researches have been done in flood disaster management and food prediction systems. However, it has now become significant to shift from individual monitoring and prediction frameworks to smart flood prediction systems which include stakeholders and the flood affecting people equally with help of recent technological advancements. Internet of Things (IoT) is a technology that is a combination of embedded system hardware and wireless communication network which further transfers sensed data to computing device for analysis in real-Time. Researches in direction of flood prediction have shifted from mathematical models or hydrological models to algorithmic based approaches. Flood data is dynamic data and non-linear in nature. To predict floods, techniques such as artificial neural networks are used to devise prediction algorithms. Here an IoT based flood monitoring and artificial neural network (ANN) based flood prediction is designed with the aim of enhancing the scalability and reliability of flood management system. The main aim of this system is to monitor humidity, temperature, pressure, rainfall, river water level and to find their temporal correlative information for flood prediction analysis. The IoT approach is deployed for data collection from the sensors and communication over Wi-Fi and an ANN approach is used for analysis of data in flood prediction. © 2017 IEEE.