The increasing complexity of modern computing systems and the demand for high performance, efficiency, and reliability have driven the adoption of machine learning (ML) techniques for predictive system optimization. This study explores the role of ML models in analyzing system behavior, forecasting performance trends, and enabling proactive optimization of computing resources. By leveraging historical and real-time data, ML algorithms can identify patterns, detect anomalies, and predict potential bottlenecks, allowing systems to adapt dynamically to changing workloads. The paper examines various machine learning approaches, including supervised learning, unsupervised learning, and reinforcement learning, in the context of system optimization. Techniques such as regression models, decision trees, neural networks, and clustering algorithms are analyzed for their effectiveness in tasks such as resource allocation, workload prediction, energy optimization, and fault detection. The integration of ML with cloud computing, edge computing, and distributed systems is also discussed, highlighting its role in enabling intelligent and autonomous system management. Furthermore, the study addresses challenges such as data quality, model interpretability, computational overhead, and integration complexity. Strategies such as feature engineering, model tuning, and continuous learning are explored to improve model performance and reliability. The findings suggest that machine learning-driven predictive optimization significantly enhances system efficiency, reduces operational costs, and improves overall system resilience, making it a critical component of modern intelligent infrastructures.