Maintaining road surface integrity is vital for autonomous driving systems and vehicle safety. Traditional inspection methods are labor intensive, time consuming and subjective, highlighting the need for an efficient, automated process for the identification of road surface defects. In the proposed work, we present an effective method for identifying road surface defects using three-dimensional (3 \(D\) ) point cloud information. By employing 3 \(D\) geometric analysis, such as surface normal and curvature’s calculation and apply an adaptive machine learning strategy, we accurately detect defects, even small minutes-level defects such as cracks and holes. Our approach combines geometric analysis with artificial intelligence (AI) through machine learning (ML) to classify defects based on point-cloud data, leading to a reliable and robust surface defects detection approach. This approach significantly improves the performance of the defect detector compared to the state-of-the-art detector on our custom dataset. This advancement promises to streamline road surface maintenance, ensuring the safety and reliability of humans and autonomous driving.