Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In conjunction with the computational shortcut of kernel functions, the SVM classifier maps input data from the input space into the high-dimensional feature space, and seeks an optimal hyperplane to separate data from different classes. Both ANN and SVM machine learning algorithms can be used to establish nonlinear relationships between variables and rank the importance of variables, thereby, contributing to the effectiveness of medical curriculum assessment. The purpose of this investigation is to shed light on how to construct the most suitable ANN and SVM curriculum assessment models based on student perceptions. These are then compared with logistic regression. Participants were 216 graduating medical students, representing a 90% response rate, each of whom took part in a survey in years 2006, 2007, or 2008. The outcome variable of interest was student satisfaction or dissatisfaction with the overall basic science curriculum. Twelve independent variables included student agreement that the basic science curriculum is responsive to student feedback, open to innovation, well-coordinated, and integrated to prepare future physicians for complex clinical problem-solving. Important variables found in the ANN and SVM models were highly significant for curriculum assessment and development. These results were consistent with the logistic regression model. Moreover, the classification accuracy of the ANN and SVM were compared to the logistic regression model based on criteria of sensitivity, specificity, combined accuracy, and the F-measure. It is evident that the resulting models of the ANN and SVM have demonstrated the model applicability, validity, and accuracy for curriculum assessment. Therefore, the researcher recommends ANN and SVM modeling approaches be applied to curriculum assessment in institutions of higher education. SVM Classifier is appended. (Contains 3 figures and 5 tables.)