This paper proposed a new optimization technique to black-box software testing by integrating Honey Badger Algorithm (HBA) with the decision table approach. The decision table considers the system requirements and automatically creates a collection of test cases out of system procedures. These cases are forwarded to the honey badger algorithm, which chooses the optimum possible cases according to the principles of exploration and exploitation in order to increase test coverage and decrease the total number of test cases while maintaining test efficiency. Black box testing is a effective strategy that tests software systems on the basis of input and output, with no knowledge of internal mechanisms. HBA, is algorithm inspired by the foraging strategies of honey badgers, can be used to increase the test process's efficiency and effectiveness. The proposed tool utilizes the features of both HBA and the decision table approach to automatically derive the most optimal test cases, ensuring testing full coverage with all possible input and output scenarios. This integration not only improves the overall quality of the testing process, but also addresses the challenge of achieving complete system testing coverage with fewer test cases. By combining and hybrid these techniques, the paper presents a novel solution that enhances both the speed and accuracy of black-box testing, making it an indispensable automated tool for testing complex and large-scale software systems.