<![CDATA[The rapid advancement of computer systems has increased the complexity and performance demands of computer hardware, leading to higher risks of hardware failure. Early detection of hardware faults is crucial to ensure system reliability, reduce downtime, and minimize maintenance costs. This proceeding discusses the application of Machine Learning (ML) techniques in computer hardware failure detection systems as an intelligent and adaptive solution. Machine Learning enables automated analysis of large volumes of hardware monitoring data, such as temperature, voltage, power consumption, and error logs, to identify patterns that indicate potential failures. Various ML approaches, including supervised learning, unsupervised learning, and anomaly detection methods, can be utilized to predict hardware malfunctions before critical failures occur. Compared to traditional rule based monitoring systems, ML based detection systems offer higher accuracy, scalability, and the ability to adapt to dynamic hardware environments. Furthermore, the integration of Machine Learning with hardware sensors and monitoring tools enhances real time fault detection and supports predictive maintenance strategies. This paper highlights the role, advantages, and challenges of applying Machine Learning in computer hardware failure detection systems, including issues related to data quality, model interpretability, and computational overhead. Overall, the application of Machine Learning provides a promising approach to improving the reliability and efficiency of modern computer hardware systems.]]>
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