Automatic pothole detection is important for improving road maintenance and transportation safety. While image-based pothole detection often struggles under poor lighting and weather conditions, point cloud data provides a robust alternative by capturing detailed surface geometry. Machine learning has demonstrated strong performance in point cloud classification. While traditional machine learning is simpler and relies on handcrafted features, deep learning models are more powerful, as they learn complex, high-dimensional patterns directly from the input data. While most existing work relies on deep learning models, which are time-consuming to train and require extensive labelled datasets, potholes can be well described by geometric features, making pothole detection well-suited for feature engineering. This paper compares traditional machine learning and deep learning approaches for pothole classification using point cloud data, to evaluate whether the added complexity and data demands of deep learning models are justified, or if traditional machine learning techniques are sufficient for accurate classification. A dataset with labelled pothole instances is created to train both models. The machine learning approach uses manually engineered geometric features as input to an ensemble classifier, while the deep learning model is trained on sampled data. Experimental results show that the machine learning approach outperformed the deep learning model. These results suggest that for this particular task, where informative domain-specific features can be manually engineered, the machine learning approach offers a more practical and efficient solution for real-world deployment, where labelled data may be limited.