ISI Journals

This article explores the synergistic integration of neural networks, machine learning, and applied mathematics to enhance predictive analytics. The aim is to demonstrate the potential of combining these methodologies to achieve more accurate and robust predictions in various fields. The research methodology involves a comprehensive literature review to understand the current state of the art, followed by the development and implementation of a novel framework that incorporates neural networks and machine learning algorithms with advanced mathematical models. The results indicate significant improvements in predictive accuracy, highlighting the promising avenues for future research and practical applications.