Computational Math Modeling and AI Optimization for Better Decision-Making: Applications in Machine Learning

Abstract

The integration of computational mathematical modeling, optimization, machine learning (ML), data analysis, and artificial intelligence (AI) offers transformative opportunities to solve complex problems across diverse fields. This paper delves into the development and application of advanced modeling techniques that blend traditional mathematical approaches with advanced AI algorithms to enhance predictive accuracy, optimize resource allocation, and improve decision-making processes in areas such as logistics, finance, engineering, and healthcare. By leveraging data-driven insights, these models can simulate real-world scenarios and identify optimal solutions more effectively than conventional methods. The paper also explores how AI-enhanced modeling can impact broader systems by reshaping industry practices, influencing frameworks, and potentially challenging established societal norms. Eventually, the paper argues for a balanced and informed deployment of AI-driven modeling techniques to maximize their benefits while addressing potential risks and limitations.