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Abstract Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values forroot mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE), andnormalized mean squared error(NRMSE) are$$23.22 \pm 6.39$$ $23.22\pm 6.39$mg/dL, 16.77 ± 4.87 mg/dL,$$12.84 \pm 3.68$$ $12.84\pm 3.68$and$$0.08 \pm 0.01$$ $0.08\pm 0.01$respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of$$80.17 \pm 9.20$$ $80.17\pm 9.20$and$$84.81 \pm 6.11,$$ $84.81\pm 6.11,$respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.