The management of long-term conditions like type-1 diabetes relies on accurate glucose forecasting to optimize patient care. In this paper, we present a novel ensemble neural network (NN) model for glucose prediction in type 1 diabetes patients. The proposed ensemble model consists of three different types of recurrent networks (RNN). A fuzzy weighted averaging integration method is employed to compute the final prediction. The proposed ensemble NN model accomplishes a mean root mean square error (RMSE) of 0.001921 ( \(\pm\) 0.001077), based on 30 experiments done using data generated from the UVA/Padova simulator. To further enhance the model’s performance, we conducted an optimization of the model’s architecture using a genetic algorithm which then obtained an RMSE of 0.001416 ( \(\pm\) 0.000545). The genetic algorithm was employed to find the best architecture for the ensemble model, including the number of layers, neurons, LSTM cells, and other parameters. The comparative analysis highlights the performance of our proposed ensemble NN and how it improves the results of existing ensemble models. These findings emphasize the potential of our model in advancing glucose prediction accuracy so that patients can make better decisions when managing their condition.