Tsunami detection in the coastal area is a major problem and a big impact on the environment, therefore early detection and training for tsunami need to be carried out to reduce the impact of casualties and losses incurred. Machine learning techniques, with their ability to identify patterns and anomalies, offer an approach for tsunami detection. This paper introduces a novel tsunami detection model combining the Chicken Swarm Optimization (CSO) Algorithm and Artificial Neural Network (ANN) classifier. Using the data pre processing technique such as data cleaning and data visualization the input dataset is processed and featured using speedup data transformation the raw data is converted in to certain data using feature engineering. This hybrid approach effectively analyses the detection of tsunami by leveraging the strengths of both methods. The model is trained and evaluated using the earthquake dataset, achieving a commendable accuracy of 92%. The software used here is python. The proposed model demonstrates superior results compared to existing techniques, offering an efficient solution for environment.