Hybrid Machine Learning Models for Predictive Business Intelligence in Enterprise Systems

Abstract

Hybrid machine learning models have emerged as a powerful approach for enhancing predictive business intelligence within enterprise systems by combining the strengths of multiple learning techniques. Traditional single-model approaches often struggle with complex, large-scale, and heterogeneous enterprise data, leading to limitations in prediction accuracy and adaptability. This paper explores the use of hybrid machine learning models that integrate methods such as supervised learning, unsupervised learning, deep learning, and ensemble techniques to improve predictive performance and decision support in enterprise environments. The study highlights how hybrid models enable more accurate forecasting, improved pattern recognition, and scalable analytics across enterprise functions. By embedding hybrid machine learning into business intelligence systems, organizations can achieve more robust, flexible, and actionable insights that support data-driven decision-making and strategic planning