Abstract – This work aimsat examining the use of AI in anticipation of diabetes and the use of dataanalysis. To achieve this, we trained different classifiers using a broadDiabetes Detection Dataset which includes the following; Logistic Regression, KNearest Neighbor (KNN), Random Forest Classifier, and Decision Tree Classifier.The observation made from the analysis showed that the Random Forest Classifieryielded the highest overal accuracy of 96. It was found that 82% of thepopulation was affected, with a precision of 0.94 and a recall of 0.69 forpositive cases. The overall performance of KNN model was also impressive it hada precision of 0.91 and a recall of 0.62, while LR and DTC gave usefulinformation about the data but did not perform so well in some of theevaluation measurements. Some of these items included correlation heatmaps andROC curves that helped in capturing the relationship between diabetes and otherhealth aspects such as blood glucose level, HbA1c and model performance. Itconfirms that the concept enshrined in the AI-technologies actually has a hugepotential in the early diagnosis and intervention programs that will eventuallylead to efficiency enhancement and harmonization of health care servicesdelivery. Future research should hence focus on issues to do with thegeneralizations of the models as well as ways of combining data in order toenhance the level of predictive and health care improvement. This studypredicts diabetes with the aid of machine learning models. Exploratory dataanalysis shows that the major predictors of diabetes are age, blood glucose,and hypertension. The implementation of the models Logistic Regression, KNN,Random Forest, and Decision Tree was done. Random Forest turned out to be thebest model being very accurate and precise in predicting negative cases andquite reasonable in performance for positive case detection. This researchcontributes to the early detection of diabetes and potentially better treatmentfor patients.