Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin deficiency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above “normal,” defined as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error. The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes. Historically, research has supported the use of various machine algorithms, such as naïve Bayes, decision trees, and artificial neural networks, for early diagnosis of diabetes. However, to achieve maximum accuracy and minimal error in diagnostic predictions, there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals. Therefore, in this paper, we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate final decisions regarding early diagnosis of diabetes.