A Comprehensive Review of Machine Learning and Optimization Techniques for Scalable Decision Support Systems

The use of artificial intelligence systems has expanded into multiple fields. The systems experience major difficulty when they attempt to achieve both rapid performance and precise results. The systems require abilities to process multiple data streams simultaneously. The research paper investigates how Artificial Intelligence technology functions in decision-making processes and optimization operations. The research shows how machine learning combined with Genetic Algorithms and Particle Swarm Optimization improves system performance. The research paper investigates the existing limitations of present-day techniques. The study uses accuracy and execution duration as performance metrics to assess system effectiveness. The research paper investigates the difficulties that arise when dynamic data needs to be processed through these particular methods. The research paper demonstrates multiple research deficiencies which exist because scientists have conducted limited research on Artificial Intelligence and optimization method combinations while they have studied real-time systems to a minor extent. There is no standard way to measure performance. The research paper demonstrates a requirement for systems that can operate at both high efficiency and large capacity because it proposes the creation of hybrid optimization techniques which will benefit real-time computing systems.