Chronic diabetes afflicts millions of individuals. Regular surveillance and regulation are necessary to prevent issues. The advent of technology has led to a transformation in healthcare, with a focus on utilizing data to detect diseases, track their progression, and develop personalized treatment approaches. Utilizing Big Data analytics, particularly in healthcare clouds, enables the analysis of extensive patient data to enhance diabetes management. Conventional methods for detecting diabetes and planning diets rely on rule-based algorithms or basic machine learning models. These systems may lack the ability to record intricate data connections or promptly react to changes in patient health. They may not fully harness the potential of cloud-based, extensive healthcare data. Nevertheless, current diabetes detection and diet planning systems often lack the advanced capabilities to effectively handle the intricate nature and unpredictability of patient data. The vast amount of healthcare data stored in cloud systems poses challenges in terms of processing and retrieving pertinent information. Enhancing the accuracy, efficiency, and customization of diabetes care requires a more sophisticated approach. Contemporary analytics and machine learning techniques are necessary to enhance the identification of diabetes and offer individualized dietary suggestions for every patient. This endeavor aims to develop a user interface and cloud model utilizing an ensemble architecture to identify diabetes and plan diets. This innovation has the potential to transform healthcare analytics. Ensemble frameworks excel at capturing intricate data patterns, resulting in enhanced accuracy for predicting diabetes and planning diets. Ensemble models are highly effective in handling the dynamic and diverse nature of big data clouds in the healthcare industry because of their strong resilience and capacity to adapt. Ensemble frameworks have the ability to efficiently handle large volumes of healthcare data, allowing for real-time analysis and decision-making. The potential for transformation to enhance the precision, adaptability, and efficiency of diabetes treatment enhances patient care and results