Millions of people are affected by the prevalent disease of diabetesDiabetes worldwide, and women are the population group most affected by this condition. Having diabetesDiabetes raises the chances of developing other health illnesses such as heart disease, kidney disease, nerve damage, and blindness due to the damage it causes to blood vessels. Accurately diagnosing diabetesDiabetes by analyzing relevant data remains a major challenge. Modern healthcareHealthcare research uses innovative and advanced technologies to diagnose individuals and detect their illnesses based on clinical data. Machine learning (ML)Machine learning is a technology that can improve the accuracy of diagnosis and detection. In this work, we use the female Pima Indians Diabetic DatasetPima Indian diabetes dataset employing several supervised learning algorithms such as Support Vector Machine (SVM)Support Vector Machine (SVM), Random Forest (RF)Random Forest (RF), Decision Tree (DT)Decision Tree (DT), and Logistic RegressionLogistic regression (LR) to detect diabetesDiabetes. Additionally, we used GridSearchCV for hyperparameter tuningHyperparameter tuning to enhance the model’s performance and experimented with various parameters. We evaluate the F1 score, precision, and recall for all supervised learning techniques. The predictive accuracy was 92.17%, which was the best accuracy achieved by RFRandom Forest (RF), and in comparison with other research, it showed great promise for addressing the limitations in this field. RF performed exceptionally well and demonstrated its superiority over other MLMachine learning classifiers. Furthermore, the proposed study aims to enhance the quality of life for people who have diabetesDiabetes and also foster research development.