Brain tumors represent a significant proportion of cancers in humans, with an incidence that continues to rise. Glioblastoma, the most aggressive tumor, demonstrates a variable response to treatment. Patients diagnosed with glioblastoma have a median survival of 15 months. A major challenge is that treatment efficacy, evaluated by anatomical MRI, becomes apparent more than two months after initiation. Given the limited survival time, early identification of non-responders before treatment onset is crucial. A binary classification model was performed on a cohort diagnosed with glioblastoma and treated between 2018 and 2023 at our center. Initially, treatment efficacy prediction was assessed using only the surgical criterion. The obtained sensitivity, specificity, and accuracy were 79.78%, 59.30% and 69.71%, respectively. Subsequently, a classifier was pre-trained using transfer learning on the ResNet-51Q model. This model takes as input nine central slices of pre-treatment MRI per patient. The results obtained on the test set were 79.10%, 90.74%, and 81.68% for sensitivity, specificity, and accuracy respectively. Deep hybrid learning (DHL) models were trained to include clinical data, with 84.38%, 94.74% and 90.00% for sensitivity, specificity, and accuracy respectively. Compared with the criterion of surgery alone, the deep learning approach improves the prediction of treatment efficacy prior to its administration. We enhanced performance by incorporating clinical data. Using models to predict treatment efficacy in GBM patients from pre-treatment data has considerable potential for personalising treatment regimens.