Predicting road surface normal and pitch with image-based algorithms remains a significant challenge, especially when steep inclines, declines, and sudden changes in road inclination are involved. To solve this problem, we propose a novel image-based algorithm that leverages homography decomposition to achieve accurate ground plane normal and pitch prediction. By integrating a Kalman Filter, our method enhances prediction stability. Further, we incorporate robust sensor fusion by integrating IMU-based odometry, ensuring that the estimates are accurately aligned with real-world motion. Overall, our approach outperforms existing techniques in both accuracy and responsiveness for dynamic driving environments. Experimental results show that our approach achieves superior performance, reducing average pitch and normal errors by 0.493^∘ and 0.483^∘, respectively, compared to the current state-of-the-art, and exhibits a shorter transient response in case of a sudden road inclination change. Code is available at: https://norbertmarko.github.io/ground-normal-prediction/.