One of the most basic phases of the software development life cycle (SDLC) is software testing. Testing takes up around half of the time needed to release a product. Code-based testing takes a lot of time and is prone to mistakes. Automated testing by using a Modelbased approach is therefore more favored. The design and development of actual test cases as well as the priority level classification of the generated test cases are the two main concerns in software testing. By rapidly locating a small number of test cases that must satisfy an adequacy condition, automated test case generation techniques attempt to lower the cost of software testing and contribute to more effective testing of software products. most of the current test case generation and classification approaches are based on the system code, even though model-based test case generation methodology which is the subject of this research work becomes an obvious choice in the software development industry. Automatic test case Generation and classification remain major issues in software testing. To fill this gap, we have proposed a model that uses DFS algorithms for generating the test cases and a machine learning approach used for priority-level classification of the generated test cases from the UML Activity diagram. The proposed model for the test case generation tool has been evaluated effectively and it generated all the possible test cases from activity diagrams by satisfying the test coverage criteria condition. The study used three machine learning algorithms to classify the generated test cases based on their priority level and each algorithm achieved an accuracy of SVM 89.73%, KNN 85.63%, and NB 73.93%. as we can see from the results, SVM outperformed the other two machine learning algorithms. Key Words: UML Diagram, Test Case, Generation, Classification Prioritization, Activity Diagram, Activity Dependency Table, Activity dependency Graph, Testing Coverage criteria.