Diabetes is a chronic disease affecting millions globally, necessitating the development of early detection tools to mitigate severe complications such as heart disease, kidney failure, and nerve damage. This project focuses on creating a cloud-based machine learning system to predict diabetes risk using the Pima Indians Diabetes Database. The problem addressed is the need for scalable and efficient early detection methods, critical for improving patient outcomes and reducing healthcare costs. The proposed system employs Logistic Regression, Random Forest, and Support Vector Machines to predict diabetes and analyze key risk factors. A significant aspect of this main is utilizing cloud resources, specifically Google Cloud TPUs (Tensor Processing Units) and GPUs (Graphics Processing Units), which enhance model training speed and scalability. TPUs and GPUs are specialized hardware accelerators designed to handle the intensive computations required for machine learning (ML) tasks, enabling faster processing of large datasets and complex algorithms. Performance evaluation demonstrates the effectiveness of the current study, with the Support Vector Machines model achieving the highest accuracy of 75%, precision of 79%, F1-Score of 82%, and recall of 85%. The cloud-based deployment showed a 40% reduction in processing time compared to traditional server-based solutions, highlighting the system’s scalability and efficiency in handling large datasets for early diabetes detection and management.