This project is to utilize cloud computing in deploying an ML-enabled Flask application for the prediction of water quality. The model, trained on historical datasets of water quality, is integrated into a user-friendly web interface constructed using Flask. Specifically, the main objective is to explore the deployment and hosting of the applications across the AWS cloud platform to ensure scalability, reliability, and efficiency. Other deployment methods scrutinized include AWS Elastic Beanstalk, AWS Lambda, and EC2. For deploying, Elastic Beanstalk was adopted as the principal deployment because it can natively host the application and handle scalability while providing end-to-end management of infrastructure. However, issues arose with regards to Lambda’s resource limitation in handling gigantic ML models, while configuration problems of EC2 were inevitable in scaling. The project itself reflects strengths and limitations of these services and points towards very robust infrastructure in resource-intensive ML applications. It’s applicable not only in the scalable solution of real-time water quality prediction but also comparative with studies on deploying Flask applications within AWS, with insights to optimize performance and future applications based on cost-effectiveness.