Climate change is now a reality, aided by the continued and increased release of greenhouse gases such as carbon dioxide, methane, etc., due to global warming. For the flood-prone state of India, which is one of the most important catastrophic events, it aims to develop an early-warning system to reduce the risk due to floods. During the previous two decades, machine-learning (ML) methods to mimic the complex mathematical expressions of the physical processes of Flooding made a major contribution to the creation of prediction systems that offered better performance and reasonable solutions. However, by using fresh ML techniques and incorporating presently existing methods in this paper, the researchers aim to find more accurate and efficient predictive models. As a result, this paper presented the most comprehensive flood estimation methods for both long-term and short-term floods. The main objective of this paper is to improve a forecasting model for flood occurrence using an algorithm in Python programming language and display the obtained results in the form of a web application. In the future, there are possibilities for further investigation in this field to find out how much climate change will increase, and the intensity and frequency of catastrophic weather events including floods, cyclones, droughts, heat waves, etc.