<p>Recent advancements in Transformer-based monocular 3D object detection techniques have exhibited exceptional performance in inferring 3D attributes from single 2D images. By incorporating depth information and visual features from images, these methods enhance spatial awareness, playing a crucial role in applications such as autonomous driving and augmented reality. However, most existing methods rely on resource-intensive Transformer architectures, leading to significant drops in computational efficiency and performance when handling long sequence data. To address these challenges, we propose MonoMM, a Multi-scale Mamba-Enhanced network for real-time monocular 3D object detection. This architecture includes the Focused Multi-Scale Fusion module for effectively preserving and fusing image information from different scales with lower computational resource consumption, and the Depth-Aware Feature Enhancement Mamba module for globally integrating depth information and visual information. Extensive experiments on the KITTI dataset show that MonoMM outperforms previous monocular methods with a 3D average precision increase of 6.39% in the easy setting and achieves real-time detection at 29.0 FPS. The modular design of MonoMM also provides high flexibility and scalability, facilitating adjustments and optimizations according to specific application needs.</p>