Diabetes is a life-threatening and long-lasting illness that produces high blood glucose levels. Diabetes may cause various diseases, including liver disease, blindness, amputation, urinary organ infections, etc. This research work aims to introduce a hybrid framework to enhance outcomes predictability and interoperability with reduced ill-posed problems, over-fitting problems, and class imbalance problems for diagnosing diabetes mellitus using data mining techniques. Diabetes may be recognized in many ways. One of these methods is data mining techniques. The use of data mining to medical data has yielded meaningful, significant, and effective results that may improve medical expertise and decision-making. This study suggests a hybrid technique for detecting DM that combines the lasso regression algorithm with the artificial neural network (ANN) classifier algorithm. The Lasso regression technique is used for variable selection and regularization. Because the dataset was shrunk, the computing time was considerably minimized. The ANN classifier received the Lasso regression output as an input and classified patients correctly as diabetic and non-diabetic, i.e., tested positives and negatives. The Pima Indians dataset was used in this experiment, consisting of 768 samples of female participants who are diabetic and non-diabetic. According to experimental observations, the proposed hybrid technique achieved 93% classification accuracy for predicting diabetes mellitus. The experimental results showed that our proposed method had a classification accuracy of 93% for determining whether a patient has diabetes or not. The experimental outcomes demonstrated that a hybrid data-mining approach might assist clinicians in making better diagnoses when identifying diabetes patients.