In the rapidly evolving realm of computer technology, seamless system start-up is crucial for maintaining operational efficiency and minimizing downtime. This study introduces an expert system develoed to diagnose and resolve computer system startup problems effectively. Using a combination of artificial intelligence (AI) techniques and a detailed knowledge base, the system aims to replicate human expert decision-making in troubleshooting. Initial testing involved a dataset of 96 cases, with the system achieving an accuracy and precision of 92.71%, and a recall of 93.68%. Subsequent refinement of the system was evaluated on an expanded dataset of 246 cases, resulting in improved metrics: an accuracy of 98.78%, precision of 99.17%, and a perfect recall of 100%. The error rate was significantly reduced from 7.23% to 1.22%. These results demonstrate the system's enhanced reliability and efficiency in diagnosing startup issues, underscoring the potential of expert systems in reducing the impact of startup failures, enhancing user satisfaction, and supporting high-stakes decision-making processes in computing environments. The integration of AI and expert knowledge not only streamlines the troubleshooting process but also enhances the adaptability of diagnostics to accommodate the complexities of modern computer systems