In recent years, there has been a significant increase in global demand for near actual facts on natural disasters. Time is vital during a disaster event in order to evacuate vulnerable people at risk, minimize the socio-economical, ecological and cultural impact of the event and restore society to normal as soon as possible. In this project, "Smart Early flood monitoring system using Internet of things (IoT)", an intelligent system is proposed that monitors variety of natural phenomena in order to forecast a flood so that people may prepare for it and limit the damage it causes. The system monitors environmental elements, which include humidity, temperature, water level, water increase rate, and rainfall, to detect a flood. Then the flood prediction is done using IoT enabled sensors data and machine learning with the help of MATLAB. Time Series Forecasting is used in this project to describe an innovative technique to flood prediction. To analyze, the previously acquired runtime data of a given period of time is taken by the prediction model in the form of a dataset and flood risk is anticipated. The project was successfully demonstrated through hardware implementation. When a flood occurred during the testing stage of the product, a flood was successfully detected. Using Wireless Fidelity (WIFI) technology, it can send data to the server as soon as possible. All of the detection alarm systems functioned normally. This system successfully triggered an alarm upon detection. Time Series Forecasting prediction model that assisted in predicting the next few days' flood and rain outcomes, were relatively accurate. Because the device is not overly expensive, the system is adequate and accurate for this critical task.