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Diabetes remains one of the common chronic diseases around the world and finding a good early prediction from clinical and lifestyle data is very important for timely help. Classical machine learning models such as regression, decision trees and random forests have been used for this purpose but the complicated non‑linear connections among clinical features might need richer ways of representation. Quantum Machine Learning (QML) gives an approach using quantum feature encoding and variational circuits to find patterns that are expensive to express with classical methods. In this study we show a diabetes prediction pipeline that compares baseline models—logistic regression, decision tree and random forest—with two quantum classifiers built in Qiskit: a Quantum Support Vector Classifier (QSVC) and a Variational Quantum Classifier (VQC). Using the Pima Indians Diabetes dataset we test all models on accuracy, precision, recall and F1‑score. The results show that the classical Random Forest classifier reaches the accuracy (0.73) beating both quantum classifiers. The Quantum Support Vector Classifier (QSVC 0.66) and the Variational Quantum Classifier (VQC 0.65) are still competitive. Do not outdo the classical baselines. These results agree with reports that in the present Noisy Intermediate‑Scale Quantum (NISQ) era quantum models usually match rather than surpass well‑tuned classical approaches on standard health‑care data and that a real quantum advantage will need larger data, deeper circuits and testing, on real quantum hardware.