This study addresses the challenge of gas and volatile organic compound detection using semiconductor gas sensors, with a focus on mitigating the effects of sensor drift caused by aging and chemical degradation. Machine learning methods demonstrate high accuracy in gas concentration prediction within a single measurement series, achieving reliable regression performance. However, when applied to new data from different measurement series, the models suffer a complete loss of predictive ability due to inter-series variations. Traditional preprocessing methods, including PCA, fail to improve model transferability, highlighting the limitations of linear approaches. In contrast, nonlinear dimensionality reduction techniques—particularly autoencoder-based methods—show promise in identifying stable response patterns, leading to modest improvements in cross-series regression accuracy (especially for gases with long dynamic responses). Despite these advances, performance on independent series remains significantly inferior to within-series results, underscoring the need for further development of feature extraction methods. Future work should prioritize identifying invariant features in nonlinear latent spaces (e.g., autoencoder outputs) to enhance model robustness against sensor drift and inter-series variability.