Anxiety and depression are psychological disorders characterized by persistent and impairing symptoms. They affect milions of people worldwide and have a significant impact on individuals' well-being and daily functioning. Although highly effective treatments exist, delayed diagnoses and limited access to mental health care contribute to a significant number of undiagnosed individuals. Therefore, it is important to explore predictive modeling to anticipate and address potential issues before the symptoms increase. In that context, this study proposes a machine learning approach to predict anxiety and depression scores based on the Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9). In a regression scenario the proposed multi-layer perceptron (MLP) achieved the lowest MAE values of 5.3924 for anxiety and 5.06 for depression, as well as the lowest MAPE values of 0.1101 for anxiety and 0.1043 for depression. For a classification scenario the best-performing models were the random forest (RF) and LightGBM with an F1-score of 0.8997 and 0.8918 for anxiety, respectively, and 0.7593 and 0.7480 for depression. These results highlights the potential of neural network-based models to outperform traditional ensemble and kernel-based approaches to predict mental disorder scores. Additionally, the classification results also suggests that tree and kernel-based models can effectively maintaining balanced predictive performance.