We have introduced a dynamic multifactor approach to prioritize test cases, leveraging a Multilayer Perceptron neural network. This method accurately estimates the damage for each set of factors assigned to the test case through classification. Subsequently, test cases are prioritized in a descending order based on their damage values. With an overall accuracy of 93.88%, our approach demonstrates enhanced efficiency in practical scenarios for damage value evaluation. To validate the prioritization sequence, we conducted a case study using a real application, specifically a Shopping Mart on GitHub, and compared the results with non-prioritized and randomly ordered sequences. The proposed prioritization technique exhibited significant improvements over both non-prioritized and randomly ordered sequences of the test suite. The integration of a Neural Network in the multifactor approach improves the prediction of factor impact on test cases, leading to a more precise assessment of potential damage. Furthermore, our proposed approach, incorporating a neural network, is adaptable for future implementations, offering flexibility with the inclusion of additional factors.