Accurate landslide susceptibility assessment (LSA) is essential for regional disaster prevention and mitigation planning. Although machine learning and deep learning models have been widely adopted for LSA, their transferability across regions is often compromised by distributional discrepancies between source and target datasets. To address this challenge, we adapted the conditional domain adversarial network (CDAN) framework for cross-regional landslide susceptibility assessment to mitigate domain shift and improve cross-regional robustness. Two cross-regional transfer experiments were conducted to compare CDAN with conventional machine learning, direct-transfer deep learning, and representative domain adaptation methods. The results showed that CDAN achieved the highest mean Accuracy and F1 score among the benchmark models in both target domains, with Accuracy values of 0.77 and 0.80 and F1 scores of 0.78 and 0.80, respectively. The corresponding values of the area under the receiver operating characteristic curve (AUC) were 0.837 and 0.836, respectively. Neighborhood-based spatial information also improved the direct-transfer performance of conventional machine learning models compared with single-pixel representations. These results indicate that the CDAN-based framework can reduce the reliance on labeled target-domain samples and support county-level cross-regional landslide susceptibility assessment under domain shift.