<p>The constantly growing threat of diabetes has intensified the need for early detection and prevention. This systematic review, following the PRISMA guidelines, analyzes 41 peer-reviewed studies after screening 593 preliminary records by predefined inclusion and exclusion criteria and an eight-point quality checklist published in 2021–2025 that use the Pima Indians Diabetes Dataset (PIDD). Reported accuracies are highly diverse, ranging from 77.08% to 100.00%. In most cases, ensemble techniques such as stacking, together with feature engineering, were much more effective than traditional classifiers. Some major methodological deficiencies have been observed: Most of the evaluations were conducted on intra-datasets and lacked testing on external datasets. The mechanisms of interpretability have been explored in very few studies; merely 14.60% of studies have done so. However, explainable AI techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), were taken up with the lowest prevalence of 7.30%. Although the most popular predictors are Glucose, BMI, and Age, due to the limited sample size (<i>n</i> = 768) of this study and the demographics of the PIDD, overfitting is an actual concern. Whereas the ensemble models score high, the scores are not often applicable to real clinical practice because of data issues and transparency. We have found that research studies that report near-perfect accuracy are probably the result of methodological leakage, and not an actual increase in predictive ability. We discover that marginal gains in performance are not worth their external validation, explainability or clinical trust. We propose that future studies need to be longitudinal cohort validation, leakage-safe, and making the models explainable to be trusted by doctors.</p>