Diabetes is a chronic condition that affects millions of people worldwide, presenting significant health and economic challenges. If not treated, it can lead to serious complications that severely impact a patient’s quality of life. Early prediction of diabetes is crucial. Timely diagnosis and treatment can prevent the development of other serious health issues. This study utilized the well-known Pima Indians dataset, commonly used in machine learning for predicting diabetes. Three classification algorithms—Naïve Bayes, Random Forest, and Logistic Regression—were implemented and compared within two different environments, Python and ML.NET. Additionally, the artificial neural network was implemented in Python, and the Model Builder tool in ML.NET was used to recommend a suitable machine learning model. In total, eight machine learning models were developed, compared, and tested for diabetes prediction across the two environments. The same training and test data were used for all the created models to ensure a valid comparison. This work examines the results closely to determine how various machine learning frameworks impact model performance.