Within the healthcare sector, the application of machine learning is gaining prominence, notably enhancing the efficiency and precision of diagnostic procedures. This study focuses on this key area of diabetes prediction and aims to develop an innovative prediction method. Using the data set published by Kare, this paper constructs and compares various intelligent systems based on multilayer algorithms, and specifically introduces improved reptile search algorithm (IRSA) to optimize the weight and threshold initialization of traditional backpropagation (BP) neural networks. This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. The experimental results showed that the IRSA-BP algorithm performed the best among all the evaluated algorithms, with an accuracy of up to 83.6%, showing its superior performance in diabetes prediction. Therefore, the IRSA-BP classifier has an important potential for application in the medical field. It can assist medical professionals to identify diabetes risk earlier and assess the condition more accurately, thus improving diagnostic efficiency and accuracy. This is important for early intervention and treatment of patients with diabetes and to improve their health status and quality of life.