Ensuring quality and reliability in software development is crucial, as bugs or software faults can compromise system operation and security. To provide timely solutions and enhance product quality, it is vital to sort and classify these defects effectively during the software development life cycle. This study explores the classification of software defects into distinct groups using Support Vector Machines (SVMs), Random Forests (RFs), and Decision Trees (DTs) to enhance the accuracy and efficiency of bug management. SVMs are beneficial for classifying bugs due to their ability to handle high-dimensional feature spaces and capture various characteristics of software defects. Our findings show that SVM achieves an accuracy of 85.33%, DT achieves 95.41%, and RF achieves the highest accuracy of 97.41%. This demonstrates that Random Forests outperform the other two algorithms, making it the most effective option for bug categorization.