Computer networks are indispensable in today's digital infrastructure, and hence reliability and fault tolerance are imperative for them. Failure in computer networks can cause widespread disruptions, with attendant financial and operational losses. To improve fault detection, machine learning methods like the Random Forest algorithm have found extensive application due to their resilience and accuracy. This study explores the use of the Random Forest algorithm for failure detection in computer networks, examining its efficacy in network anomaly classification, failure prediction, and minimizing false alarms. Through the application of ensemble learning, Random Forest improves the accuracy of prediction and offers insightful information regarding network health. Comparative analysis with other machine learning algorithms, including Support Vector Machines and Neural Networks, accentuates the strengths and weaknesses of Random Forest in network failure detection. Research directions for the future involve combining Random Forest with deep learning models and enhancing real-time fault diagnosis systems.