Seismic fault segmentation presents a challenging and time-consuming task in geophysics, particularly in the exploration and extraction of oil and natural gas. Deep Learning (DL) methods have shown significant potential to address these challenges and offer advantages compared to traditional methods. However, DL-based approaches typically require a substantial amount of labeled data, which contradicts the current scenario of limited availability of labeled seismic data. To address this limitation, researchers have explored synthetic data generation as a potential solution for unlabeled real data. This approach involves training a model on labeled synthetic data and subsequently applying it directly to the real dataset. However, synthetic data generation encounters the domain shift problem due to the complexity of real-world geological situations, resulting in differences in distribution between synthetic and real datasets. To mitigate the domain shift issue in seismic fault detection, we propose a novel approach utilizing Unsupervised Domain Adaptation (UDA) techniques. Our proposal involves using a synthetic dataset for model training and adapting it to two publicly available real datasets found in the literature. The chosen UDA techniques include Maximum Mean Discrepancy (MMD), Domain-Adversarial Neural Networks (DANN), and Fourier Domain Adaptation (FDA). MMD and DANN aim to align features in a common n-dimensional feature space by minimizing discrepancy and increasing domain confusion through adversarial training, respectively. On the other hand, FDA transfers the style from real to synthetic samples using Fast Fourier Transform. For the experiments, we utilized a smaller version of UNet and its variant Atrous UNet, which incorporates Dilated Convolutional layers in its bottleneck. Furthermore, DexiNed (Dense Extreme Inception Network), a state-of-the-art model for edge detection, was employed to provide a more comprehensive analysis. Additionally, we studied the application of fine-tuning on labeled datasets to investigate its impact on performance, as many studies have employed it to reduce domain shift. The final results demonstrated significant improvements in fault detection performance by applying UDA techniques, with up to a 13 percent increase in evaluation metrics such as Intersection over Union and F1-score on average. Moreover, the proposed approach achieved more consistent detections of seismic faults with fewer false positives, indicating its potential for realworld applications. Conversely, the application of fine-tuning did not show a significant gain in performance but did reduce the training time.