Machine learning optimization has emerged as a transformative approach for managing and enhancing the performance of large-scale engineering networks and intelligent automation systems. With the increasing complexity of modern infrastructures such as power grids, transportation systems, communication networks, and industrial automation platforms, traditional optimization techniques often struggle to handle high-dimensional data, dynamic environments, and real-time decision-making requirements. Machine learning models, particularly deep learning, reinforcement learning, and evolutionary algorithms, provide adaptive, scalable, and data-driven solutions that enable efficient optimization of these systems. This paper explores the integration of machine learning techniques in optimizing large-scale engineering networks, focusing on system efficiency, resource allocation, predictive maintenance, and automation intelligence. The study adopts a multidisciplinary approach combining computational intelligence, systems engineering, and data analytics to evaluate how machine learning models improve operational performance and decision-making processes. The findings indicate that machine learning optimization significantly enhances system adaptability, reduces operational costs, and enables real-time responsiveness in complex engineering environments. Furthermore, intelligent automation systems powered by machine learning demonstrate improved autonomy, fault tolerance, and scalability, making them essential for future smart infrastructures. The research highlights the growing importance of integrating machine learning optimization frameworks into engineering systems to achieve sustainable, efficient, and intelligent automation.