Diabetes mellitus (DM) is a persistent metabolic disorder typically diagnosed through various methods such as blood glucose tests, oral glucose tolerance tests, and HbA1c measurements. Early identification is pivotal for efficient management and the prevention of complications like kidney issues, heart diseases, and nerve damage. Machine learning (ML) and feature selection (FS) techniques play a crucial role in DM detection, employing data-driven approaches to identify pertinent features from datasets comprising medical histories, clinical measurements, and patient demographics. This research introduces the gray wolf optimizer with machine learning enabled diabetes mellitus recognition (GWOML-DMR) model. The GWOML-DMR model is developed to identify DM by merging FS and parameter tuning methods. Initially, variable stability scaling (VSS) is implemented to standardize the data uniformly. The model utilizes the gray wolf optimizer (GWO) for efficient feature selection, while a feedforward neural network (FNN) is employed for DM detection. Moreover, the hyperparameter tuning method, particle swarm optimization (PSO), enhances DM recognition within the FNN. GWOML-DMR augments detection rates, simplifies recognition processes, and contributes to risk assessment, ultimately leading to enhanced patient outcomes. Validation utilizing the PIMA Indians Diabetes dataset substantiates its superior performance in DM detection.