The increasing incidence of Type 2 diabetes necessitates developing good diagnosis methods for the improvement of early detection and intervention strategies. This research aimed at the challenge of classifying Type 2 diabetes accurately using clinical blood test data based on a bio-inspired machine learning approach. For this research, a dataset of 2,300 clinical blood test records was obtained with appropriate ethical clearance, and the presence of diabetes was predicted using different machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). Features were optimized using a Genetic algorithm (GA) to improve the performance of the model by selecting the most relevant variables. In the results, it was shown that the highest accuracy was obtained by the GA + ANN model at 98.67%; the precision, recall, and F1 scores were superior to the rest. The second-highest accuracy was acquired by the model GA + SVM at 94.50%. The accuracies obtained by the GA + DT and GA + RF models were 92.30 and 91.11%, respectively. In conclusion, this implies that the models learned using bio-inspired algorithms for feature selection are substantially more robust and interpretable. The implication of the applications of this study has significant impacts on clinical practice since the optimized machine learning models can be used to provide support in the timely intervention and tailor-made treatment plans in the early detection of Type 2 diabetes.