We present MPDG-SLAM, a novel 3D Gaussian point cloud rendering SLAM method based on Motion Probability (MP) for dynamic interference handling. Current 3DGSSLAM approaches for dynamic environments often rely on optical flow estimation masks. However, these deep learning-based optical flow models are computationally intensive and limited by processing speed, posing challenges for deployment on mobile devices in real-world scenarios. Moreover, existing systems depend on precise mask segmentation and corresponding loss functions for artifact removal, yet the pixel accuracy of optical flow estimation is constrained by real-world lighting conditions. To address these issues, we introduce a mobile-deployable Yolo and a mathematically derived Motion Probability (MP) attribute to label Gaussian points, which are then inversely mapped to the front-end feature tracking system to correct for dynamic object influences. By incorporating an MP-based penalty term, dynamic Gaussians corresponding to moving entities are explicitly removed to minimize their effect. Additionally, we design an edge warp loss based on MP estimation, enabling accurate artifact removal even with coarse segmentation masks. The experiments show that our approach notably improves the reconstruction quality of dynamic scenes, surpassing baseline methods and reaching speeds over 30 FPS on high-end GPUs, which suggests its potential for real-time use on mobile platforms after further optimization.