System performance analysis is a critical aspect of modern computing environments, where applications operate across distributed, cloud-based, and resource-intensive infrastructures. Traditional performance monitoring techniques often struggle to handle the scale, complexity, and dynamic behavior of such systems. Machine learning (ML) techniques provide a powerful alternative by enabling intelligent analysis of large volumes of performance data, uncovering patterns, and predicting system behavior. This study explores the application of various ML techniques, including supervised learning, unsupervised learning, and reinforcement learning, in analyzing and optimizing system performance. It examines how ML models can be used for anomaly detection, workload prediction, resource allocation, and fault diagnosis. The paper also discusses the integration of ML with real-time monitoring systems to enable proactive and adaptive performance management. Key challenges such as data quality, model interpretability, scalability, and computational overhead are analyzed along with potential solutions. The findings highlight that ML-driven performance analysis significantly improves system efficiency, reliability, and scalability in complex computing environments.