Bipolar Disorder (BD) is a psychiatric condition diagnosed by repetitive cycles of hypomania and depression. Since diagnosing BD relies on subjective behavioral assessments over a long period, a solid diagnosis based on objective criteria is not straightforward. The current study responded to the described obstacle by proposing a hybrid GAN-CNN model to diagnose BD from 3-D structural MRI Images (sMRI). The novelty of this study stems from diagnosing BD from sMRI samples rather than conventional datasets such as functional MRI (fMRI), electroencephalography (EEG), and behavioral symptoms while removing the data insufficiency usually encountered when dealing with sMRI samples. The impact of various augmentation ratios is also tested using 5-fold cross-validation. Based on the results, this study obtains an accuracy rate of 75.8%, a sensitivity of 60.3%, and a specificity of 82.5%, which are 3-5% higher than prior work while utilizing less than 6% sample counts. Next, it is demonstrated that a 2- D layer-based GAN generator can effectively reproduce complex 3D brain samples, a more straightforward technique than manual image processing. Lastly, the optimum augmentation threshold for the current study using 172 sMRI samples is 50%, showing the applicability of the described method for larger sMRI datasets. In conclusion, it is established that data augmentation using GAN improves the accuracy of the CNN classifier using sMRI samples, thus developing more reliable decision support systems to assist practitioners in identifying BD patients more reliably and in a shorter period