Despite the great success of machine learning in various biomedical domains, applications to dental hard tissue conditions (primarily on dental Caries, Erosive Tooth Wear (ETW), and Fluorosis) are under-explored, in particular for analyzing photographic images. The clinical diagnostics of these dental hard-tissue conditions is routinely performed by visual examination but is often limited by its subjectivity. To bridge this gap, we apply four categories of machine learning strategies including nine different methods with two different feature representations to estimate the probability and severity of dental hard-tissue conditions from photographic tooth images. Our first empirical study is performed on the real dataset containing both controls and cases, and the best probability estimation results are achieved by Extra Trees Regression (RMSE: 0.010, Pearson correlation: 0.581) for Caries, Decision Tree (RMSE: 0.058, Pearson correlation: 0.615) for ETW, Bayesian ARD Regression (RMSE: 0.068, Pearson correlation: 0.765) for Fluorosis. Our second empirical study is performed on the case only datasets, and the best severity estimation results are achieved by Extra Trees Regression (RMSE: 0.010, Pearson correlation: 0.684) for Caries, Decision Tree (RMSE: 0.063, Pearson correlation: 0.542) for ETW, Bayesian ARD Regression (RMSE: 0.085, Pearson correlation: 0.610) for Fluorosis. These results indicate that machine learning models provide promising opportunities to help clinical evaluation and save resources in the management of these dental conditions.