Tractography from diffusion MRI enables the mapping of white matter pathways. A key challenge is the automatic segmentation of streamlines into anatomically meaningful bundles, which is strongly affected by class imbalance: common bundles dominate the training data, whereas bundles such as the fornix are severely underrepresented (<0.2% of streamlines). This work proposes a tractography segmentation framework that combines a generative oversampling strategy based on Variational Autoencoders (VAEs) with a streamline-wise convolutional classifier operating on FiberMap representations. The generative model is applied in a targeted manner only to underrepresented bundles, and its effect is compared with two alternative oversampling schemes: simple sample duplication and the Synthetic Minority Oversampling Technique (SMOTE). Experiments on the Tractoinferno dataset, comprising 32 white matter bundles, show that all configurations yield similar global performance, with macro F1-scores around 90–91%. In contrast, performance on underrepresented bundles changes markedly, with VAE-based oversampling improving left/right fornix F1-scores from 77.2%/68.7% to 90.4%/92.5%, yielding an average gain of 18.5 percentage points across the two fornix bundles. Sample duplication increases recall but substantially reduces precision, while SMOTE achieves the highest recall at the cost of more false positives. A complementary three-dimensional geometric analysis revealed a fidelity–coverage trade-off, with SMOTE more closely reproducing real length and curvature distributions while VAE samples provided greater coverage of real trajectories. Overall, the VAE achieved the most favorable precision–recall balance for the targeted fornix bundles. These results suggest that targeted VAE-based oversampling is a practical strategy for mitigating extreme imbalance in tractography segmentation, improving the recognition of underrepresented bundles without materially affecting performance on dominant bundles.