The quick development of information and the expanding complexity of large-scale data frameworks require progressed optimization methods to guarantee tall execution, versatility, and unwavering quality. Machine learning (ML) has developed as a effective apparatus for real-time optimization, advertising the capacity to memorize from tremendous sums of information and make data-driven choices with minimal human mediation. This term paper investigates the different applications of machine learning in optimizing large-scale data frameworks in genuine time, covering perspectives such as prescient analytics, peculiarity discovery, arrange activity administration, substance conveyance optimization, security, and personalized proposal frameworks. The paper points to supply a comprehensive outline of how machine learning can be utilized to upgrade the productivity, adaptability, and flexibility of these complex frameworks.