The prevalence of diabetes as a global health crisis requires innovative approaches for early detection and prevention. Our study presents a comprehensive examination of multilayer neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), multilayer perceptron’s (MLPs) and traditional regression models, with the aim of improving the accuracy of diabetes prediction. What sets this research apart is the extensive application of eXplainable Artificial Intelligence (XAI) tools such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and ALE (Accumulated Local Effects) to elucidate the contribution of individual features and the inner workings of these machine learning models. This level of detailed feature importance analysis and elucidation of predictive mechanisms is uncommon in the complex realm of current machine learning applications. Our study offers a twofold contribution by advancing the methodology for diabetes prediction modeling with a strong emphasis on interpretability, and it centers on the discussion around AI that clinicians can interpret and trust. The insights derived from this research could significantly impact diagnostic modeling in healthcare and establish a benchmark for future investigations into the harmonization of AI deployment with transparency.