Face sketch-photo synthesis (FSPS) is a challenging task with practical applications in scenarios such as criminal investigation. Existing methods often struggle to balance the global dependency modeling and computational efficiency. To better address this issue of FSPS, we propose a novel Multi-Conditional Prior-guided Dual Spiral CNN-Mamba Network (MCPDS-CMNet), which is composed of three key components: a Semantic Prior Dual Spiral CNN-Mamba (SemPDS-CM), a Texture Prior Dual Spiral CNN-Mamba (TexPDS-CM), and a Color-Integrated Feature Fusion (CIFF) module. SemPDS-CM and TexPDS-CM are designed to disentangle semantic and texture feature representations. By independently enhancing feature representations from two complementary perspectives, these two modules facilitate more faithful structure reconstruction and enhance the consistency and visual realism of generated images. In SemPDS-CM and TexPDS-CM, we propose a Dual Spiral Scan (DS-Scan) mechanism to replace conventional scanning methods, aiming to enhance the two models’ capability for modeling long-range dependency in facial structures. CIFF is used for fusing multi-source features such as semantics and texture. In CIFF, we propose an HSV color prior-guided feature fusion mechanism to compensate for color information is often lost during sampling, which leads to more vivid and perceptually natural image synthesis. Extensive experiments on four public datasets confirm that our proposed MCPDS-CMNet achieves competitive performance in synthesis quality and perceptual consistency, validating its effectiveness and excellence.