Abstract
This study envisions the future trajectory of intelligent optimization and machine learning (ML) in the realm of business analytics, introducing novel perspectives. It investigates the synergy between big data analytics and ML, underscoring the effectiveness of deep learning architectures in unravelling complex patterns. Emphasizing interpretability, the study explores the development of ML models tailored for business contexts and delves into decentralized model training and data privacy through edge computing and federated learning. In the optimization domain, it addresses the ascendancy of customized meta-heuristic algorithms and explores the convergence of optimization and ML for heightened operational efficiency. This research contributes to a nuanced understanding, fostering innovative applications in the dynamic landscape of business analytics. It has been observed that machine learning and intelligent optimization techniques are very useful for business analytics.
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